Investigating the effects of climate change and global warming caused by GHG emissions have been a primary concern worldwide. These emissions are largely contributed to by the production, use and disposal of consumer products. Thus, it is important to build tools to estimate the environmental impact of consumer goods, an essential part of which is conducting Life Cycle Assessments (LCAs). LCAs specify and account for the appropriate processes involved with the production, use, and disposal of the products. We present SpiderGen, an LLM-based workflow which integrates the taxonomy and methodology of traditional LCA with the reasoning capabilities and world knowledge of LLMs to generate the procedural information used for LCA. We additionally evaluate the output of SpiderGen using real-world LCA documents as ground-truth. We find that SpiderGen provides accurate LCA process information that is either fully correct or has minor errors, achieving an F1-Score of 62% across 10 sample data points. We observe that the remaining missed processes and hallucinated errors occur primarily due to differences in detail between LCA documents, as well as differences in the "scope" of which auxiliary processes must also be included. We also demonstrate that SpiderGen performs better than several baselines techniques, such as chain-of-thought prompting and one-shot prompting. Finally, we highlight SpiderGen's potential to reduce the human effort and costs for estimating carbon impact, as it is able to produce LCA process information for less than \$1 USD in under 10 minutes as compared to the status quo LCA, which can cost over \$25000 USD and take up to 21-person days.

翻译：研究由温室气体排放引起的气候变化和全球变暖效应已成为全球关注的核心议题。这些排放主要源于消费品的生产、使用和处置过程。因此，开发用于评估消费品环境影响的工具至关重要，其中开展生命周期评估（LCA）是核心环节。LCA通过明确界定并量化产品生产、使用及处置过程中涉及的各项流程，系统评估其环境影响。本文提出SpiderGen——一种基于大语言模型的工作流程，该流程将传统LCA的分类体系和方法论与大语言模型的推理能力及世界知识相融合，以生成用于LCA的流程信息。我们进一步使用真实LCA文档作为基准对SpiderGen的输出进行评估。研究发现，SpiderGen能提供准确（完全正确或仅含微小错误）的LCA流程信息，在10个样本数据点上达到62%的F1分数。我们观察到，未覆盖的流程及生成错误主要源于LCA文档间的细节差异，以及对必须包含的辅助流程“范围”界定的不同。实验同时证明，SpiderGen在性能上优于思维链提示、单样本提示等基线方法。最后，我们强调SpiderGen在降低碳影响评估人力成本方面的潜力：相较于当前耗时可达21人日、成本超过25000美元的LCA现状，该系统能在10分钟内以低于1美元的成本生成LCA流程信息。