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塞马网:在生命周期评估中预测缺少的环境数据的桥梁词和数字
Bin Chen1, Hong Chen2, Zhishan Quan3
1School of Electrical and Electronic Engineering, University of Sheffield, Sheffield S1 4DT, U.K.
Environmental science & technology
|September 26, 2025
概括
本研究介绍了SemaNet,一种使用语言模型预测生命周期评估 (LCA) 缺失的环境数据的新方法. 它准确地填补了数据缺口,即使缺少了100%的信息,也改善了可持续性评估.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 生命周期评估 (LCA) 对于环境影响评估至关重要.
- 缺乏生命周期库存 (LCI) 数据严重限制了LCA的有效性.
- 目前的LCI数据补充方法在与数据稀缺性作斗争.
研究的目的:
- 开发一个新的框架来完成LCI数据.
- 为了利用定性过程描述进行定量环境流动预测.
- 克服现有的基于数值相关的方法的局限性.
主要方法:
- 提出了一个基于语义的神经网络框架,SemaNet.
- 利用预训练的语言模型来弥合定性描述和定量环境流.
- 实现语义过以减少计算要求.
主要成果:
- 在预测缺失的LCI值方面,SemaNet取得了卓越的性能.
- 该方法表现出高精度,即使缺少100%的数值数据.
- 与现有方法相比,计算要求降低了99%.
结论:
- 在LCI数据完成方面,SemaNet提供了一个范式的转变.
- 显著减少了LCA从业人员的数据收集工作和时间.
- 在各个行业中实现可靠,更快的环境影响和可持续性评估.
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