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用生成AI进行生命周期评估的排放因子建议
Bharathan Balaji1, Fahimeh Ebrahimi1, Nina Gabrielle G Domingo2
1Amazon, Seattle, Washington 98121, United States.
Environmental science & technology
|March 21, 2025
概括
本研究介绍了一种人工智能驱动的工具,用于自动化温室气体 (GHG) 排放因子选择,用于生命周期评估. 该方法提高了环境影响量化的准确性和效率,有助于实现净零目标.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 工业生态学 工业生态学
背景情况:
- 准确的温室气体 (GHG) 量化对于环境影响评估和减缓战略至关重要.
- 生命周期评估 (LCA) 依赖于排放因子 (EFs) 来估计间接排放,这一过程目前是手动的,耗时的,容易出现错误的.
- 手动EF选择需要大量的专业知识,并可能阻碍环境报告的可扩展性.
研究的目的:
- 开发和验证人工智能辅助的方法,用于自动化温室气体排放因子 (EF) 建议.
- 在生命周期评估 (LCA) 中提高EF选择的准确性,效率和可扩展性.
- 支持组织在其可持续发展倡议和实现净零排放目标的进展.
主要方法:
- 利用自然语言处理 (NLP) 和机器学习 (ML) 来创建自动EF推的算法.
- 开发了一个系统,为推的EFS提供人类可解释的理由.
- 实施了一种分层方法,允许专家协助或完全自动化的EF选择.
主要成果:
- 人工智能辅助的方法在完全自动化模式下实现了86.9%的平均精度,用于正确的EF建议.
- 该方法在前10个建议中确定了正确的EF,平均精度为93.1%.
- 在多个现实世界数据集中证明了有效性,证实了该方法的稳定性.
结论:
- 人工智能辅助的方法显著简化了LCA的EF选择过程.
- 这种方法使得温室气体排放量能够得到更可扩展和更准确的量化,从而促进了企业的可持续发展努力.
- 该工具通过提高环境影响评估的可靠性来支持组织实现净零排放目标.
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