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

2025-11-11

AI Summary

Carbon life cycle assessment (LCA) suffers from labor-intensive, costly, and time-consuming manual process modeling. Method: This study proposes the first LLM-based automated process generation framework for LCA, integrating the ISO 14040/44-compliant LCA classification system with the world knowledge of large language models (LLMs), leveraging prompt engineering and structured process modeling. Accuracy is quantified via F1-score against ground-truth processes extracted from real LCA documentation. Contribution/Results: Evaluated on 10 diverse case studies, the framework achieves a 62% F1-score; most outputs are either fully correct or contain only minor deviations. Each analysis costs under USD 1 and completes in under 10 minutes. Compared to conventional expert-driven approaches and chain-of-thought prompting, this method substantially reduces human effort while advancing accuracy, computational efficiency, and accessibility—establishing a scalable new paradigm for rapid carbon footprint assessment of consumer products.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Estimating environmental impact of consumer goods through Life Cycle Assessments
• Automating LCA procedure generation using LLMs to reduce human effort
• Addressing high costs and time requirements of traditional LCA methods

Innovation

• LLM-based workflow for life cycle assessment
• Integrates LCA taxonomy with LLM reasoning
• Generates LCA procedures using generative AI