揭示人工智能和绿色技术融合对碳排放的影响:一种可解释的基于机器学习的方法
Tianlong Shan1, Shuai Feng1, Kaijian Li2
1School of Management Science and Real Estate, Chongqing University, Chongqing, 400045, China.
Journal of environmental management
|December 11, 2024
概括
人工智能 (AI) 和绿色技术的融合大大减少了碳排放. 更高的融合网络效率和创新团队规模对减排产生积极影响,后者显示出U形效应.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 技术管理 技术管理
背景情况:
- 绿色技术和人工智能 (AI) 对于减少碳排放至关重要.
- 技术融合通过人工智能和绿色技术集成加速了低碳目标,这在智能系统中可以看到.
- 了解这种趋同的机制对于有效的气候战略至关重要.
研究的目的:
- 调查人工智能和绿色技术融合机制如何影响碳排放.
- 从属性和网络中提取和分析收特征.
- 量化这些特征对碳排放的个人和互动影响.
主要方法:
- 利用了来自中国县级城市 (1997-2019) 的面板数据.
- 采用极端梯度提升 (XGBoost) 算法进行预测建模.
- 应用沙普利添加剂解释 (SHAP) 用于特征效应分析.
主要成果:
- 技术融合普遍性和创新团队规模显著影响碳排放.
- 创新团队规模证明了碳排放的U形效应.
- 高收网络效率与碳排放抑制正相关.
结论:
- 人工智能和绿色技术的融合为减少碳排放提供了一个可行的途径.
- 政策干预应考虑促进融合普遍性和优化创新团队规模.
- 提高融合网络效率对于那些致力于降低碳足迹的城市来说至关重要.
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