## 3 Results and Discussion
### How to Choose the Optimal Solution within Each POS?
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\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**WIS2**} & \multicolumn{4}{c}{**W2S2**} & \multicolumn{4}{c}{**W2S3**} & \multicolumn{4}{c}{**W3S2**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Primary Energy**} & \multicolumn{4}{c}{**Primary Energy**} & \multicolumn{4}{c}{**Primary Energy**} & \multicolumn{4}{c}{**Primary Energy**} & \multicolumn{4}{c}{**Primary Energy**} & \multicolumn{4}{c}{**Primary Energy**} \\  & \multicolumn{2}{c}{**Consumption**} & \multicolumn{2}{c}{**LCC (f/m\({}^{3}\))**} & \multicolumn{2}{c}{**Consumption**} & \multicolumn{2}{c}{**LCC (f/m\({}^{3}\))**} & \multicolumn{2}{c}{**Consumption**} & \multicolumn{2}{c}{**LCC (f/m\({}^{3}\))**} & \multicolumn{2}{c}{**Consumption**} & \multicolumn{2}{c}{**LCC (f/m\({}^{3}\))**} \\  & \multicolumn{2}{c}{**(W/km/m\({}^{3}\))**} & \multicolumn{2}{c}{**(W/km/m\({}^{3}\))**} & \multicolumn{2}{c}{**(W/km/m\({}^{3}\))**} & \multicolumn{2}{c}{**(W/km/m\({}^{3}\))**} & \multicolumn{2}{c}{**(W/km/m\({}^{3}\))**} & \multicolumn{2}{c}{**(W/km/m\({}^{3}\))**} & \multicolumn{2}{c}{**(W/km/m\({}^{3}\))**} & \multicolumn{2}{c}{**(W/km/m\({}^{3}\))**} \\ \cline{2-13}  & **FR** & **SP** & **FR** & **SP** & **FR** & **SP** & **FR** & **SP** & **FR** & **SP** & **FR** & **SP** & **FR** & **SP** & **FR** & **SP** \\  & **POS9** & **POS153** & **POS9** & **POS153** & **POS10** & **POS10** & **POS14** & **POS10** & **POS11** & **POS15** & **POS11** & **POS15** & **POS11** & **POS15** & **POS12** & **POS16** & **POS12** & **POS16** \\ \hline
6 & 38.09 & 51.59 & 90.25 & 143.27 & 33.26 & 71.29 & 117.12 & 195.15 & 44.24 & 97. Nevertheless, considering all results of efficient frontiers and values of primary energy consumptions, it is not so clear which solution is able to combine all factors affecting the choice to do retrofitting interventions. For this reason and to explain better the results, we propose the scatterplots for each typology of building and climate zone, where the efficiency scores, calculated with LCC (i.e., holistic efficiency scores, Model#1) represent the values on abscissa axis and the values of the primary energy consumption are placed on ordinate axis. Figure 1 presents the methodology applied: we have used the LCC estimation as input for the estimation of DDF with CO2 emissions as undesirable output and the final energy saving as desirable one. Results are holistic efficiency scores representing the capability of solution to minimize emission and maximizing the final energy saving (taking equal the LCC). Finally, we crossed these scores with the primary energy consumption that is another crucial variable to consider in the choice of retrofitting interventions. These figures with scatterplots allow to evaluate the best solution considering the holistic efficiency scores and the primary energy consumptions. These representations clearly suggest choosing the solutions near to the origin of axes where the holistic efficiency is at its maximum and the primary energy consumption is at its minimum. The bullet points in the figures represent the solutions of each POS, as described in the near labels. Figure 2 shows the plot referring to the Single-Family houses and climate zone W1S2. In this case the graph confirms previous considerations and for Cyprus the solution 9 (POS1) is the best one as in the case of Croatia (POS5). Figure 1: Schema of adopted methodology. Figure 3 considers W2S2 climate zone and if for Cyprus solutions 5, 6, and 8 of POS2 can be considered as optimal; on the contrary, Croatia presents an optimal combination of efficiency scores and consumptions for solution 1 of POS6, confirming results obtained in previous analysis. Figure 2: Plot of holistic efficiency scores and primary energy consumptions (kWh/m\({}^{2}\)) for Single-Family Houses and W1S2 climate zone. ((**a**) Cyprus in blue dots; (**b**) Croatia in red dots).