Artificial intelligence, green transition and green total factor productivity in enterprises

Coupled with swift advancement of artificial intelligence (AI), relationship between AI and an enterprise's green total factor productivity (GTFP) is receiving increasing attention. This study conducts an empirical analysis of the relationship between AI and GTFP in Chinese enterprises over the period 2007-2022. The results demonstrate that AI development significantly enhances GTFP, with enterprise green transformation serving as a key mechanism. Further decomposition of GTFP indicates that AI contributes to improvements in both green technical efficiency and green technological progress. The findings also reveal significant heterogeneity across different types of AI technologies: while computer vision and machine learning have a strong positive impact on GTFP, logic-based AI does not exhibit a meaningful effect. Moreover, there is notable heterogeneity across regions and the positive effect of AI on GTFP is more pronounced in western regions of China. In addition, the impact of AI varies by firm characteristics-such as whether firms are labor-intensive, technology-intensive, or capital-intensive.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-09-26
DOI
https://doi.org/10.1038/s41598-025-18878-y
Citations
2
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
6.26
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial intelligence, green transition and green total factor productivity in enterprises

Huan Li
2 citations
Scientific Reports
Energy, Environment, Economic Growth
6.26
article

Artificial intelligence, green transition and green total factor productivity in enterprises

Huan Li
article en
2 citations

Abstract

Coupled with swift advancement of artificial intelligence (AI), relationship between AI and an enterprise's green total factor productivity (GTFP) is receiving increasing attention. This study conducts an empirical analysis of the relationship between AI and GTFP in Chinese enterprises over the period 2007-2022. The results demonstrate that AI development significantly enhances GTFP, with enterprise green transformation serving as a key mechanism. Further decomposition of GTFP indicates that AI contributes to improvements in both green technical efficiency and green technological progress. The findings also reveal significant heterogeneity across different types of AI technologies: while computer vision and machine learning have a strong positive impact on GTFP, logic-based AI does not exhibit a meaningful effect. Moreover, there is notable heterogeneity across regions and the positive effect of AI on GTFP is more pronounced in western regions of China. In addition, the impact of AI varies by firm characteristics-such as whether firms are labor-intensive, technology-intensive, or capital-intensive.

Scientific ReportsVol. 15(1)
Fuzhou University (CN), Fujian University of Technology (CN)
Openalex Percentile: Top 4%
Energy, Environment, Economic Growth
6.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.