| 人工智能对减污降碳协同增效的影响 |
| 摘要点击 974 全文点击 21 投稿时间：2025-10-22 修订日期：2025-12-16 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 人工智能（AI） 减污降碳协同增效 偏效应 门槛效应 空间溢出 |
| 英文关键词 artificial intelligence（AI） synergistic pollution and carbon reduction bias effects threshold effects spatial spillover |
| DOI 10.13227/j.hjkx.202510240 |
|
| 中文摘要 |
| 随着新一代信息技术的兴起，人工智能在提升环境治理效能和促进绿色发展方面展现出巨大潜力. 基于2014~2023年中国267个地级市面板数据，运用双向固定效应模型和空间杜宾模型等多种计量模型，深入探讨人工智能对减污降碳协同增效的影响及其作用机制. 结果表明，①人工智能能够显著促进减污降碳协同增效，且其促进作用存在动态变化；②产业结构升级、绿色技术创新及缓解资源错配是人工智能促进减污降碳协同增效的重要作用路径；③人工智能对减污降碳协同增效的作用效果因数字基础设施发展水平差异呈现双重门槛特征；④人工智能对减污降碳协同增效的影响具有明显的空间溢出特征，可通过技术扩散带动周边地区减污降碳协同增效. 研究不仅揭示了人工智能对减污降碳协同增效的影响机制，还为构建跨区域绿色协同治理体系提供了实证依据. |
| 英文摘要 |
| With the rise of next-generation information technologies， artificial intelligence （AI） has demonstrated substantial potential in enhancing environmental governance and promoting green development. Using panel data from 267 prefecture-level cities in China spanning from 2014 to 2023， this study applies a variety of econometric models， including the two-way fixed effects model and spatial Durbin model， to explore the impact of AI on synergistic pollution and carbon reduction and its underlying mechanisms. The results indicate that： ① AI significantly promoted synergistic pollution and carbon reduction， with its impact exhibiting dynamic changes. ②Industrial structure upgrading， green technological innovation， and resource misallocation alleviation were key pathways through which AI drove synergistic pollution and carbon reduction. ③ The effect of AI on synergistic pollution and carbon reduction showed dual-threshold characteristics due to variations in digital infrastructure development. ④ The influence of AI on synergistic pollution and carbon reduction exhibited pronounced spatial spillover effects， which could foster regional synergistic pollution and carbon reduction through technological diffusion. This study not only uncovers the mechanisms through which AI influences synergistic pollution and carbon reduction but also provides empirical evidence for the construction of a cross-regional green collaborative governance system. |
