Automating Life Cycle Assessments through Artificial Intelligence Agents and Integrated Assessment Models

Area of Science:

• Environmental science and sustainability assessment.
• Computer science and artificial intelligence applications.

Background:

• Life cycle assessment (LCA) is crucial for sustainable decision-making but is hindered by its resource-intensive nature.
• Existing literature lacks a comprehensive review of LCA automation potential.

Purpose of the Study:

• To review the potential of artificial intelligence (AI) agents and integrated assessment models (IAM) to automate life cycle assessments (LCA).
• To identify research gaps and challenges in the adoption of automated LCA.

Main Methods:

• Review of existing literature on LCA automation using AI and IAM.
• Analysis of the potential for automation across all four stages of the LCA process.

Main Results:

• AI agents and IAMs present viable methods for automating all four stages of LCA.
• Adoption of automated LCA tools within the broader LCA community remains minimal.
• Despite reported reliable results, perceived untrustworthiness hinders adoption.

Conclusions:

• Automated LCA offers significant improvements in time and resource efficiency.
• Further research is needed to rigorously assess the quality of automated LCA.
• Developing community-driven norms is essential for the trustworthy adoption of automated LCA tools.