机器学习分析了中国县级城市的碳反弹效应动态和驱动因素
1School of Economics, Guizhou University of Finance and Economics, Guiyang, 550025, China.
Scientific reports
|October 28, 2025
概括
中国中国中国中国.
科学领域:
- 环境科学 环境科学
- 气候变化研究 气候变化研究
- 能源政策 能源政策
背景情况:
- 由于碳反弹效应 (CRE),中国在减少排放方面面临重大挑战.
- 全球关注气候变化需要了解CRE对主要经济体能源效率的影响.
- 碳排放减排计划对减排的努力产生了不利影响,尤其是在像中国这样的大型能源消耗国.
研究的目的:
- 确定和分析中国碳反弹效应 (CRE) 的非线性驱动因素.
- 通过使用先进的分析方法,揭示驱动CRE的核心机制.
- 为中国的有针对性的生态政策提供科学基础.
主要方法:
- 利用了来自中国县级城市 (2010-2021) 的面板数据.
- 采用多重线性回归与合并和深度学习模型相结合.
- 应用SHAP值和ALE图表,用于可解释的变量影响和值效应分析.
主要成果:
- CRE显示了一个"M"形的波动趋势.
- CRE的空间分布遵循东>中>西和北>南的模式.
- 确定的主要驱动因素包括DEI,HTD,ER,水和UIS2,所有这些都表现出显著的值效应.
结论:
- 机器学习模型有效地识别了关键的CRE驱动因素及其非线性关系.
- 了解这些驱动因素和值效应对于中国有效的气候政策至关重要.
- 该研究为制定差异化生态政策以缓解CRE提供了科学基础.
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