# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 5 Discussion  
This research highlights the remarkable potential of blending domain-specific knowledge with cutting-edge knowledge graph techniques to improve practices in life cycle assessment. By expanding the scope of traditional LCA to include early-stage decisions of the design and engineering phases, we have demonstrated a more holistic approach that captures the full spectrum of a product's environmental impact. The development of a meticulously defined schema using a labeled property graph (LPG) model proved effective in representing the complex relationships and entities involved in a product's life cycle. This comprehensive schema facilitated the inclusion of not only the standard life cycle phases but also the intricate interactions between actors, software tools, and decision-making processes that influence environmental outcomes. Our case study on 3 DP serves as a compelling proof of concept for the proposed methodology. The 3 DP domain, characterized by rapid technological advancements and a paucity of comprehensive LCA data, presented an ideal context to test the efficacy of our approach. By constructing a KG that encapsulates 135 nodes and 128 relationships, we successfully modeled the entire life cycle of a 3D-printed product, from the initial design to end-of-life considerations. One of the most salient findings from the KG analysis is the profound influence of early-stage engineering decisions on subsequent production phases and the overall environmental impact. For example, choices made during the design phase, such as material selection and geometric configurations, were found to significantly affect material consumption, energy usage, and waste generation in the later stages. The ability to trace these influences within the KG highlights the critical importance of proactively integrating sustainability considerations at the beginning of the product development process. Secondly, through this ability to trace influences of early decisions, the focus can lie reactively on important levers for sustainability for the next product generations in order to ensure sustainable development in the future. The analytical applications of the KG further demonstrate its utility in extracting actionable insights. Through precise Cypher queries, we identified the specific roles involved in each life cycle phase, providing clarity on stakeholder responsibilities and facilitating more effective resource allocation. The aggregation of CO2 emissions data across materials, energy sources, and transportation modes enabled a quantifiable assessment of the product's environmental footprint. This comprehensive emission profiling is instrumental for organizations aiming to meet stringent sustainability targets and for policymakers formulating environmental regulations. Moreover, the KG's capability to model alternative scenarios through \"Option\" nodes allows for comparative analyses of different design and production choices. This feature allows decision-makers to evaluate trade-offs between various materials, processes, and technologies based on quantitative environmental metrics. Such analyses can inform strategic decisions that optimize both performance and sustainability, driving innovation in product development. The integration of data using advanced language models to estimate missing values represents a significant advance in addressing data gaps common in the early design phases. Using the semantic understanding of models like GPT-4, we were able to generate plausible estimates for critical environmental data, such as CO2 emissions for specific materials or processes. This approach not only accelerates the design process by providing immediate access to the necessary data but also enhances the completeness and robustness of the KG. Our validation results indicate that the language model estimations align closely with the actual data, demonstrating mean absolute errors and relative error percentages within acceptable ranges for preliminary assessments.