| 人工智能驱动农业碳减排的效应与机制：绿色金融与产业结构高级化的协同视角 |
| 摘要点击 942 全文点击 21 投稿时间：2025-12-09 修订日期：2026-02-16 |
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| 中文关键词 人工智能（AI） 农业碳减排 绿色金融 产业结构 |
| 英文关键词 artificial intelligence（AI） agricultural carbon emission reduction green finance industrial structure |
| DOI 10.13227/j.hjkx.202512128 |
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| 中文摘要 |
| 人工智能为破解农业低碳转型瓶颈提供了新路径，其通过推动绿色金融发展、加速产业结构高级化，在抑制农业碳排放中展现出关键作用. 基于绿色金融与产业结构高级化的双重视角，使用中国2000~2023年31个省域面板数据，构建双向固定效应模型实证考察人工智能驱动农业碳减排的影响效应及机制. 结果表明：①人工智能对农业碳排放具有显著且稳健的抑制效应. 经一系列稳健性检验，该结论依然稳健；②机制检验证实，人工智能能够通过促进绿色金融发展与推动产业结构高级化两条路径，间接且协同地推进农业碳减排；③异质性分析表明，人工智能的农业碳减排效应在东部地区、城镇化水平较低的地区、农业现代化水平较弱的地区及非粮食主产区的表现更为突出. 研究建议在政策层面全方位促进人工智能技术在农业领域的应用、构建绿色金融与农业结构转型的协同路径、推行差异化支持与重点突破策略，助力人工智能赋能农业绿色发展，充分释放其碳减排效能. |
| 英文摘要 |
| 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. |
