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此摘要是机器生成的。

ExioML是一个新的开源数据集,用于估计温室气体排放,改进企业的可持续发展报告和机器学习. 它提供了详细的,最新的排放因子,使其具有广泛的可用性和可重复性.

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科学领域:

  • 环境科学与经济学
  • 数据科学和机器学习

背景情况:

  • 准确的温室气体 (GHG) 排放估计对于企业的可持续性和机器学习至关重要.
  • 现有的排放因子数据集受到许可限制,时间空间细节差,信息过时的困扰,阻碍了可重复性.
  • 需要对温室气体排放因子提供开源,细粒度和最新的数据集.

研究的目的:

  • 引入ExioML,这是一个温室气体排放因子的开源数据集.
  • 为数据集成和分析提供一个计算效率高,可扩展的工具包.
  • 建立一个可重现的基线,用于对可持续性和机器学习模型进行基准测试.

主要方法:

  • ExioML 源自 Exiobase 3.8.2,集成多区域输入输出表与 GPU 加速工具包.
  • 该数据集包括49个地区的28年 (1995-2022) 的部门级排放因子.
  • 数据以产品对产品 (200类) 和行业对行业 (163类) 的格式结构化.

主要成果:

  • ExioML提供了公开可访问的排放因子表,具有增强的时空细分度.
  • 用机器学习模型定义和评估了预测部门温室气体排放的回归任务.
  • 数据集促进了与其他数据集和计算工具的兼容性和可扩展性.

结论:

  • ExioML为准确和透明的温室气体排放估计提供了一个有价值的开源资源.
  • 数据集和相关工具包支持可持续性和机器学习研究中的可复制性和基准测试.
  • ExioML的目标是推动跨学科的排放因子数据的实用性和可访问性.