[Effects and Mechanisms of Artificial Intelligence Driving Agricultural Carbon Emission Reduction： A Synergistic Perspective of Green Finance and Industrial Structure Upgrading].

Authors

Jie Hu, Jian-Chao Luo

Published in

Huan jing ke xue= Huanjing kexue. Volume 47. Issue 9. Pages 5961-5973. Sep 08, 2026.

Abstract

As China advances its carbon peaking and carbon neutrality goals， agricultural low-carbon transition has become a critical priority for reconciling ecological security and food security. While artificial intelligence （AI） offers transformative potential to address bottlenecks in agricultural green transformation， its carbon reduction mechanisms， particularly the synergistic roles of green finance and industrial structure upgrading， remain underexplored in existing literature， which often lacks integrated analytical frameworks and long-term dynamic evidence. Based on the dual perspectives of green finance and industrial structure upgrading， this study uses the panel data of 31 provinces in China from 2000 to 2023 and constructs the two-way fixed effect model to empirically test the effect and mechanism of AI driving agricultural carbon emission reduction. The results show that： ① AI had a significant and robust inhibitory effect on agricultural carbon emissions， and this conclusion remained robust after a series of robustness tests. ② The mechanism test confirmed that AI could indirectly and synergistically promote agricultural carbon emission reduction by promoting the development of green finance and the upgrading of industrial structure. ③ Heterogeneity analysis showed that the effect of AI on agricultural carbon emission reduction was more prominent in the eastern region， regions with a medium level of urbanization， a low level of agricultural modernization， and non-major grain-producing areas. The study suggests promoting the application of AI technology in the agricultural field at the comprehensive policy level， build a coordinated path between green finance and agricultural structure transformation， and implementing differentiated support and key breakthrough strategies to help AI to empower agricultural green development and fully release its carbon emission reduction efficiency.

PMID:
42765218
Bibliographic data and abstract were imported from PubMed on 21 Sep 2026.

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