## 4 Results  
Table 4 shows how many flows from each of the five sources were capable of being mapped to the FEDEFL from the total flows within the source. Mappings were possible for 85-98% of flows from these five other flow lists. Most of the flows that remained unmap-pable either contained information that was not possible to capture with the FEDEFL, such as long-term emissions in ecoinvent, or flows that were unidentifiable, such as chemicals that lacked CAS numbers. In Table 5, an analysis comparing flow names from different sources shows some of the challenges associated with interoperability. The table in Table 5 is read as the percentage of flowables found in the source list in the first column that can be found in the source list in the top row. For example, 55% of flowables from ecoinvent can be found in USLCI. Lists were viewed as having significant matching capabilities if greater than 50% of unique flowables were found to match. Lists were defined as having less than marginal matching capabilities if less than 25% of unique flowables matched. Based on name-name matching between sources, 25% of dataset comparisons had significant matches, while 56% of comparisons showed less than marginal matches. An example of significant flow matching capabilities can be found for USEEIO flowables found in openLCA, as 64% of flowables from USEEIO matched with a flowable in openLCA. IDEA was found to have marginal matching capabilities with openLCA with 31% of flowables matching. In fact, nine sources found marginal matching capabilities with openLCA and seven out of nine sources found significant matching capabilities with openLCA. openLCA had the greatest capacity for automated name-name matching (defined as the greatest number of significant and marginal matching capabilities based on openLCA column). It is to be expected that when other sources are matched with software sources, higher matching capabilities will be observed. This is because software EF lists tend to be created as an amalgamation of EFs from other sources and are more inclusive in their structure. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Ecoinvent** & **USLCI** & **OpenLCA** & **TRACI** & **ImpactWorld+** \\ \hline Mapped & 3751 & 4351 & 4605 & 3568 & 3457 \\ Total & 4323 & 4450 & 5216 & 3950 & 4048 \\ \% of flows mapped & 87\% & 98\% & 88\% & 90\% & 85\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Available mappings analysis. Databases and datasets tend to be narrower in scope and are, therefore, most likely to exhibit the lowest matching rates, as seen in the first four columns of Table 5 (under the databases and datasets header). However, when databases and datasets are matched with software sources, the larger, more inclusive nomenclatures of the software tend to allow for higher matching rates. The low number of significant matches found between databases and datasets and LCIA methods (found in the last three columns of the table) shows why it is important for mapping files to exist between sources. A lack of connectivity between databases, datasets, and LCIA methods can lead to inaccurate LCA results. Therefore, most software sources have a mapping file with LCIA methods. However, mappings can be time consuming to produce and need to be consistently updated for interoperability between sources to be maintained. When the FEDEFL is used as an intermediate mapping, more flows from USLCI and ecoinvent Figure 1 map to LCIA methods. The matching percentage between LCI databases and LCIA methods increased 38% to 277% across the different LCI and LCIA sources. The variation in improvement in matching can be attributed in large part to the framework and structure of the original sources. Some of the databases and LCIA methods have similar naming structures or nomenclature rules when compared with other sources. This could account for minimal increases in interoperability in select cases when using a common standard list.