ZJ
Z.C. Ju
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Conventional Life Cycle Assessment (LCA) is limited by its reliance on static and retrospective background datasets and by its predominantly inside-out modelling approach, where models are built strictly from internal factory operations and primary data, often missing sudden external disruptions. These limitations reduce their capability in context of the Critical Raw Material (CRM) value chains affected by geopolitical conflicts and climate-related disruptions. While external macro-events may alter Life Cycle Inventory (LCI) parameters, they are usually reported in fragmented and unstructured sources like daily news. Additionally, public news rarely reports direct LCA parameters or exact numerical changes, making it difficult to capture external events that genuinely impact LCA models. More recently, Large Language Models (LLMs) have emerged as a promising solution. However, without historical references and the capacity for complex reasoning or real-world interaction, LLMs are highly prone to fabricating or hallucinating missing numbers when asked to generate scenario values. This makes these events difficult to translate into structured, quantitative, and LCA-ready scenario inputs.
This study conducts a methodological exploration and develops an Event Knowledge Graph (EKG)-driven Agentic AI framework as a human-governed outside-in sensing layer for LCA scenario exploration. Because relying solely on LLMs to quantify qualitative news inevitably leads to numerical hallucinations, the objective is to examine how qualitative real-world macro-events can be reliably translated into structured, quantitative, and LCA-ready parameter-change scenarios without unconstrained AI guessing.
The main contribution of this research is a novel, validated, and reusable blueprint for the structured, low-hallucination risk translation between macro-disruptions in CRM value chains reported in real-time news and quantitative changes in LCI parameters. Crucially, the system explicitly positions Agentic AI as a supportive co-pilot, proving that human expertise remains indispensable in defining parameter scopes, setting scenario boundaries, and performing the final LCA interpretation. ...
This study conducts a methodological exploration and develops an Event Knowledge Graph (EKG)-driven Agentic AI framework as a human-governed outside-in sensing layer for LCA scenario exploration. Because relying solely on LLMs to quantify qualitative news inevitably leads to numerical hallucinations, the objective is to examine how qualitative real-world macro-events can be reliably translated into structured, quantitative, and LCA-ready parameter-change scenarios without unconstrained AI guessing.
The main contribution of this research is a novel, validated, and reusable blueprint for the structured, low-hallucination risk translation between macro-disruptions in CRM value chains reported in real-time news and quantitative changes in LCI parameters. Crucially, the system explicitly positions Agentic AI as a supportive co-pilot, proving that human expertise remains indispensable in defining parameter scopes, setting scenario boundaries, and performing the final LCA interpretation. ...
Conventional Life Cycle Assessment (LCA) is limited by its reliance on static and retrospective background datasets and by its predominantly inside-out modelling approach, where models are built strictly from internal factory operations and primary data, often missing sudden external disruptions. These limitations reduce their capability in context of the Critical Raw Material (CRM) value chains affected by geopolitical conflicts and climate-related disruptions. While external macro-events may alter Life Cycle Inventory (LCI) parameters, they are usually reported in fragmented and unstructured sources like daily news. Additionally, public news rarely reports direct LCA parameters or exact numerical changes, making it difficult to capture external events that genuinely impact LCA models. More recently, Large Language Models (LLMs) have emerged as a promising solution. However, without historical references and the capacity for complex reasoning or real-world interaction, LLMs are highly prone to fabricating or hallucinating missing numbers when asked to generate scenario values. This makes these events difficult to translate into structured, quantitative, and LCA-ready scenario inputs.
This study conducts a methodological exploration and develops an Event Knowledge Graph (EKG)-driven Agentic AI framework as a human-governed outside-in sensing layer for LCA scenario exploration. Because relying solely on LLMs to quantify qualitative news inevitably leads to numerical hallucinations, the objective is to examine how qualitative real-world macro-events can be reliably translated into structured, quantitative, and LCA-ready parameter-change scenarios without unconstrained AI guessing.
The main contribution of this research is a novel, validated, and reusable blueprint for the structured, low-hallucination risk translation between macro-disruptions in CRM value chains reported in real-time news and quantitative changes in LCI parameters. Crucially, the system explicitly positions Agentic AI as a supportive co-pilot, proving that human expertise remains indispensable in defining parameter scopes, setting scenario boundaries, and performing the final LCA interpretation.
This study conducts a methodological exploration and develops an Event Knowledge Graph (EKG)-driven Agentic AI framework as a human-governed outside-in sensing layer for LCA scenario exploration. Because relying solely on LLMs to quantify qualitative news inevitably leads to numerical hallucinations, the objective is to examine how qualitative real-world macro-events can be reliably translated into structured, quantitative, and LCA-ready parameter-change scenarios without unconstrained AI guessing.
The main contribution of this research is a novel, validated, and reusable blueprint for the structured, low-hallucination risk translation between macro-disruptions in CRM value chains reported in real-time news and quantitative changes in LCI parameters. Crucially, the system explicitly positions Agentic AI as a supportive co-pilot, proving that human expertise remains indispensable in defining parameter scopes, setting scenario boundaries, and performing the final LCA interpretation.