## 4 Discussion and Conclusions  
The main conclusion from the results is that the objective and \"objectifiable\" variables (acoustic and non-acoustic) explain a relatively low percentage of variance (less than 20%) in soundscape pleasantness. This means that the best way to perform an accurate evaluation of pleasantness in the acoustic environment of an existing place is to ask the users of that place about the pleasantness which they feel in the existing soundscape. This paper is based on the goal of creating methods to predict soundscape pleasantness in such a way that they can be used as a tool to support decision-making in the management of public spaces, when direct user perception cannot be ascertained. Though the percentage of variance explained by the objective and objectifiable variables is not high, the inclusion of uncommon acoustic parameters in the regression model, such as the sound source balance, and non-acoustic parameters for the characteristics of the physical environment, leads to increases in the explained variance of 62% and 48%, respectively. Moreover, including a variable which we believe to be objectifiable in the future, such as soundscape-landscape congruence, will also produce a significant increase in the explained variance in soundscape pleasantness. Therefore, one conclusion is that a wide variety of parameters contribute to explaining pleasantness in the soundscape. The need to take into account a wide range of factors in order to predict soundscape pleasantness reinforces the idea that soundscape studies should always be holistic and integrated, and based on contributions from different disciplines, among which human and social sciences play a key role as soundscape is a construct of human perception. To explain the soundscape, it is necessary to \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SSC-Pleasant**} & \multirow{2}{*}{**Included Variables**} & \(R^{2}\)**Adjusted (without SSC/LSC (with SSC/LSC (with Congruence (a)) & \(R^{2}\) Adjusted (with SSC/LSC (with Congruence (b)) \\  & & & Congruence (b) \\ \hline \multirow{8}{*}{All acoustic and non-acoustic variables} & SSC/LSC Congruence (a) & - & 0.243 (1 st) \\  & \%BLUE & 0.040 (1 st) & 0.283 (3 rd) \\ \cline{1-1}  & Traffic & 0.121 (2 nd) & 0.263 (2 nd) \\ \cline{1-1}  & Sound source balance & 0.139 (3 rd) & 0.314 (6 th) \\ \cline{1-1}  & Surface area (m\({}^{2}\)) & 0.149 (4 th) & 0.296 (4 th) \\ \cline{1-1}  & LA\({}_{\text{eq,10 min}}\) & 0.164 (5 th) & 0.305 (5 th) \\ \cline{1-1}  & LA\({}_{\text{min,1s}}\) & 0.171 (6 th) & - \\ \cline{1-1}  & LA\({}_{\text{max,1s}}\) & 0.177 (7 th) & - \\ \cline{1-1}  & No. total sources & - & 0.319 (7 th) \\ \cline{1-1}  & \% var & 17.7\% & 31.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Linear regression analysis (step-by-step method): percentage of variance (%var) of soundscape pleasantness (SSC-Pleasant) explained by acoustic and contextual variables (a) without SSC/LSC congruence (F(6.395) = 14.378; \(p\) < 0.001) or (b) including SSC/LSC congruence (F(7.386) = 27.266; \(p\) < 0.001). focus on both the acoustic and non-acoustic variables of the place (context, as defined in the ISO standard), integrating objective (acoustic and non-acoustic) and subjective variables [1; 3; 4; 13; 22]. The proof of this complexity is the fact that the congruence between the soundscape and the landscape as perceived by the user of the place is the best predictor of soundscape pleasantness. The relevance of congruence or coherence between soundscape and landscape has also been highlighted by other researchers [19; 32; 33]. Other major elements when explaining soundscape pleasantness include physical context factors. This is in line with recent literature and the developments of two ISO standards focusing on soundscape. In this study, the two main physical context variables in the analysis areas were the presence of water, quantified as the surface area covered by water, and traffic.