# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 4 Case Study: 3D Printing
### Synthetic Data Integration  
As stated in Section 3.4, in many cases, especially for specific or novel products, detailed environmental data, such as precise CO\({}_{2}\) emission values, are not readily available; therefore, we propose leveraging the semantic knowledge embedded in language models to estimate missing values. We specifically employed the gpt-4o-2024-08-06 language model.  
To evaluate the accuracy of the model estimations, we employed a validation strategy. Starting with a complete dataset where all GWP values are known, we randomly removed 10% to create a test set, with the remaining 90% serving as a contextual knowledge base. We used LangChain to generate embeddings of the 90% dataset, capturing semantic information of the variables of each entry such as material properties, module and environmental impacts, and we stored these embeddings in a vector database for efficient retrieval. For each entry in the test set, we constructed prompts that included known variables and retrieved context from similar entries, instructing the language model to estimate the missing GWP value. The language model, leveraging both its semantic understanding and the retrieved contextual information, generated estimated GWP values. We then compared these estimates with the actual values in the test set, calculating the mean absolute error (MAE) metrics and relative error percentage to evaluate the model performance.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Material** & **Modul** & **GWP (kg CO\({}_{2}\)-eq)** & **ODP (kg CFC-11-eq)** \\ \hline Wood fiber insulation & A2 & 0.367 & 7.33 \(\times\) 10\({}^{-10}\) \\ Fibre cement facade panel & C2 & 0.042 & 1.08 \(\times\) 10\({}^{-15}\) \\ Clay plaster & A5 & 7.926 & 2.30 \(\times\) 10\({}^{-15}\) \\ Fly ash & A1–A3 & 0.000 & 0.000 \\ Cement (CEM IV 42.5) & A1–A3 & 0.688 & 3.79 \(\times\) 10\({}^{-15}\) \\ Cement (CEM II 42.5) & A1–A3 & 0.795 & 3.86 \(\times\) 10\({}^{-15}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: LCI data for various materials according to Okobau.dat.  
The MAE was 2.19, which indicates the average deviation of the estimated values from the actual values. The mean relative error percentage was 23.74%. These metrics demonstrate a moderate deviation between the estimated and actual values, suggesting a reasonable degree of accuracy.  
To illustrate the distribution of original and estimated GWP values, descriptive statistics were calculated as shown in Table 2.  
The similarity in central tendencies, such as the mean and median, between the actual and estimated GWP values suggests that the estimates fall within a realistic range. However, the larger standard deviation in the original values indicates that some extreme values are present in the original dataset, which are less represented in the estimated values. This result implies that, while a language model can provide robust approximations, it may not capture extreme values with high accuracy.  
To further demonstrate how GPT-4o estimates missing GWP values, two examples of missing values were analyzed. In Table 3 the generated outputs and validation metrics for each example are presented.