Investigating the effects of climate change and global warming caused by GHG emissions have been a key 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 graphical representations of the key procedural information used for LCA, known as Product Category Rules Process Flow Graphs (PCR PFGs). We additionally evaluate the output of SpiderGen by comparing it with 65 real-world LCA documents. We find that SpiderGen provides accurate LCA process information that is either fully correct or has minor errors, achieving an F1-Score of 65% across 10 sample data points, as compared to 53% using a one-shot prompting method. We observe that the remaining 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：研究温室气体排放导致的气候变化和全球变暖效应已成为全球关注的核心议题。消费品的生产、使用和处置是这些排放的主要贡献源。因此，开发用于评估消费品环境影响的工具至关重要，而开展生命周期评估（LCA）是该过程的核心环节。LCA通过规范化和量化产品生产、使用及处置所涉及的相关流程来实现评估。本文提出SpiderGen——一种基于大语言模型的工作流，它将传统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结合，用于生成LCA关键程序信息的图形化表示，即产品类别规则过程流图（PCR PFG）。我们通过将SpiderGen的输出与65份真实LCA文档进行对比来评估其性能。研究发现，SpiderGen能提供完全正确或仅含微小错误的LCA过程信息，在10个样本数据点上达到65%的F1分数，而单样本提示方法的得分仅为53%。我们观察到，剩余误差主要源于LCA文档间的细节差异，以及对必须包含的辅助流程的“范围”界定不同。实验还表明，SpiderGen在多项基线技术（如思维链提示和单样本提示）中表现更优。最后，我们强调SpiderGen在降低碳影响评估人力成本方面的潜力：该工具可在10分钟内以低于1美元的成本生成LCA过程信息，而现行LCA方案的成本可能超过25,000美元并耗费长达21人日的工作量。