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在能源转型的场景中,通过机器学习模型预测全球温室气体减少
1School of Environment, Tsinghua University, Beijing, 100084, China; Department of Statistics and Data Science, National University of Singapore, 119077, Singapore.
Journal of environmental management
|November 15, 2024
概括
全球变暖是由温室气体排放驱动的. 这项研究预测了39个国家的排放量,表明能源转型可以减少未来的温室气体 (GHG) 水平,特别是在发展中国家.
科学领域:
- 环境科学 环境科学
- 气候变化研究 气候变化研究
- 能源政策分析 能源政策分析
背景情况:
- 温室气体 (GHG) 排放是全球变暖的主要驱动因素.
- 各国正在向可再生能源过渡,以应对气候危机.
- 以前的研究往往缺乏全球视角,忽视了各国之间的发展差异.
研究的目的:
- 预测39个主要排放国家的温室气体排放量,区分发达国家和发展中国家.
- 分析不同能源转型场景对未来排放的影响.
- 通过量化过渡效应,为气候政策制定提供信息.
主要方法:
- 开发多种能源转型场景.
- 使用随机森林 (RF) 进行特征选择.
- 采用极端梯度提升 (XGBoost) 模型,并使用贝叶斯优化 (BO) 进行预测.
- 经过验证的模型性能,以确保稳定性和准确性.
主要成果:
- 全球温室气体排放量在2016年至2021年期间增加了5.39%.
- 发展中国家的可再生能源发电量增长了327.64%.
- 从基线转向高能源转型场景的转变预计发达国家减排量为1.22%,发展中国家减排量为5.23%.
- 预计并非所有国家都能实现最佳的减排,即使在高过渡情景下也是如此.
结论:
- 能源转型显著影响全球温室气体排放轨迹.
- 能源转型的有效性因国家而异,受转型成本等因素的影响.
- 调查结果支持明智的气候政策制定,并强调需要国家具体战略.
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