Impact of AI development on green total factor productivity
Abstract The improvement of GTFP has become an important guarantee to change China’s development mode and achieve long-term stable economic growth, while the development of AI is of great significance to the development of GTFP. This paper adopts the SBM-GML method to calculate the GTFP of 30 region...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-06461-4 |
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| Summary: | Abstract The improvement of GTFP has become an important guarantee to change China’s development mode and achieve long-term stable economic growth, while the development of AI is of great significance to the development of GTFP. This paper adopts the SBM-GML method to calculate the GTFP of 30 regions in China from 2011 to 2020, and manually collects AI data to study the impact of AI on GTFP, and systematically analyze the relationship between AI and GTFP using a panel double-fixed model with a faceted threshold model. The experimental results show that: (1) There is a positive and significant effect of AI on GTFP, which increases GTFP productivity by 0.3654% for every 1% increase in AI, which still holds after a robust type test. (2) The development of AI in China’s eastern region has a greater promoting effect on GTFP. (3) Further mechanism analysis reveals that RIS and ER are two important channels through which AI influences the improvement of GTFP. (4) Threshold regression shows that AI has a single threshold effect on GTFP based on TI and LNPGDP. The promotion of GTFP by AI is higher when technological innovation is less than the threshold value of 29.95, and the promotion of GTFP by AI is insignificant when the level of economic development is less than the threshold value of 85.45. This paper deepens the knowledge and understanding of the role played in the development of AI at the macro level and provides suggestions for improving GTFP at the provincial level in China. |
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| ISSN: | 2045-2322 |