SpiderGen: Towards Procedure Generation for Carbon Life Cycle Assessments with Generative AI

Investigating the effects of climate change and global warming caused by GHG emissions have been a central focus 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 this procedural information used for LCA. We additionally apply evaluation methods for this use-case, and evaluate the output of SpiderGen with real-world LCA documents. We find that SpiderGen provides accurate LCA process information that is either fully correct or has minor errors on average 60\% of the time. 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 understanding of ``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 that SpiderGen has the potential to drastically 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 costs over \$25000 USD and take up to 21-person days.