CO2e Emissions Analysis in Life Cycle Inventory Using Knowledge Graph Modeling

Life Cycle Inventory (LCI) is commonly used to assess the environmental impacts of products. However, the complex structure of LCI data often creates challenges for effective analysis, especially in the context of climate change mitigation. Traditional tabular methods often fail to capture these structural dependencies, limiting mitigation strategies to simple emission accounting without considering systemic influence. This study proposes a knowledge graph-based approach to refine the structural representation and streamline the analysis of LCI data. Greenhouse gas emissions are converted to CO₂e using the GWP100 metric, while structural importance is quantified using the PageRank algorithm. The analysis identifies three emission categories: high (average 185.95 kg CO₂e), medium (0.58 kg CO₂e), and low (0.03 kg CO₂e). Furthermore, integrating environmental intensity with centrality metrics enables the classification of processes into strategic quadrants, distinguishing 'Systemic Leverage' points for targeted decarbonization from 'Green Backbone' nodes essential for infrastructural stability. These results offer a structured foundation for pinpointing high-impact processes and prioritizing emission reduction strategies by balancing environmental burdens with systemic dependencies.