基于机器学习的中国碳排放模型的构建和分析
Jian Sun1, Xinzi Wang2, Mengkun Liang3
1School of Economics and Management, Beijing University of Chemical Technology, No. 15 Beisanhuan East Road, Chaoyang District, Beijing, 100029, China.
Scientific reports
|April 17, 2025
概括
中国中国中国中国.
科学领域:
- 环境科学 环境科学
- 机器学习 机器学习
- 气候变化建模模型
背景情况:
- 由于高碳排放,中国面临着重大挑战.
- 了解碳排放的驱动因素对于有效的气候政策至关重要.
研究的目的:
- 开发一种机器学习框架,用于分析和预测中国的碳排放.
- 确定影响碳排放及其影响的关键因素.
- 在各种政策情景下预测未来的碳排放趋势.
主要方法:
- 利用了一个"建模+SHAP分析+场景预测"的研究范式.
- 计算了斯皮尔曼相关系数以确定重要的解释变量.
- 使用SHAP (夏普利添加式计算) 分析来量化可变贡献.
- 为未来的排放预测进行了政策场景模拟.
主要成果:
- 确定了9个与中国碳排放有显著相关的变量,包括煤炭比例和城市化速度.
- 确定能源强度 (负面影响) 和城市化率 (积极影响) 作为关键驱动因素.
- 预计碳排放量将在2022-2028年间平稳,并在2028年达到峰值.
结论:
- 该研究提供了一个强大的机器学习方法来分析碳排放驱动因素.
- 减少能源强度和可持续城市化对于减轻排放至关重要.
- 预计中国的碳排放量将在2028年左右达到峰值,到2030年达到约97.2亿.
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