## 4 Discussion
### The Interplay of IEQ, Energy Use, and Environmental Impacts
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Indoor Temperature**} & \multicolumn{1}{c}{**CO\({}_{2}\)**} & \multicolumn{1}{c}{**Lighting Level**} & \multicolumn{1}{c}{**Sound Level**} & \multicolumn{1}{c}{**Energy Use**} & \multicolumn{1}{c}{**GWP**} \\ \hline \multirow{3}{*}{Indoor temperature} & Pearson’s Correlation & 1 & \(-0.8025\)* & \(-0.1644\) & \(-0.2525\) & \(-0.8371\)* & \(-0.8401\)* \\ \cline{2-7}  & \(p\)-value & - & 0.00168 & 0.6095 & 0.4283 & 0.0006 & 0.0006 \\ \hline \multirow{3}{*}{CO\({}_{2}\)} & Pearson’s Correlation & \(-0.8025\)* & 1 & \(-0.1366\) & 0.4435 & 0.6524* & 0.6607* \\ \cline{2-7}  & \(p\)-value & 0.0017 & - & 0.6719 & 0.1486 & 0.0214 & 0.0193 \\ \hline \multirow{3}{*}{Lighting level} & Pearson’s Correlation & \(-0.1644\) & \(-0.1366\) & 1 & 0.2061 & \(-0.0176\) & \(-0.0217\) \\ \cline{2-7}  & \(p\)-value & 0.60951 & 0.6719 & - & 0.5205 & 0.95663 & 0.9466 \\ \hline \multirow{3}{*}{Sound level} & Pearson’s Correlation & \(-0.2525\) & 0.4435 & 0.2061 & 1 & \(-0.1288\) & \(-0.1117\) \\ \cline{2-7}  & \(p\)-value & 0.4284 & 0.1486 & 0.5205 & - & 0.690 & 0.7296 \\ \hline \multirow{3}{*}{Energy use} & Pearson’s Correlation & \(-0.8371\)* & 0.6524* & \(-0.0176\) & \(-0.1288\) & 1 & 0.9995* \\ \cline{2-7}  & \(p\)-value & 0.0006 & 0.0215 & 0.9566 & 0.690 & - & \(<\)0.0001 \\ \hline \multirow{3}{*}{GWP} & Pearson’s Correlation & \(-0.8401\)* & 0.6607* & \(-0.0217\) & \(-0.1117\) & 0.9995* & 1 \\ \cline{2-7}  & \(p\)-value & 0.0006 & 0.0193 & 0.9466 & 0.7296 & \(<\)0.0001 & - \\ \hline \hline \end{tabular}  
* \({}^{*}p<0.05\).  
\end{table} 
Table 3: Correlation analysis of the parameters studied. Figure 13: Interplay of IEQ, energy use, and environmental impacts. ture and CO2 levels (\(-0.8025\), \(p=0.00168\)), suggesting that increased indoor temperatures are associated with lower CO2 concentrations. This may be linked to ventilation patterns, reduced occupancy during warmer periods, or opening windows. Conversely, a significant positive correlation was identified between CO2 levels and both energy use (0.6524, \(p=0.0214\)) and GWP (0.6607, \(p=0.0193\)), indicating that higher CO2 levels are associated with increased energy consumption and GWP. Furthermore, an exceptionally strong positive correlation was observed between energy use and GWP (0.9995, \(p<0.0001\)), reflecting a near-perfect linear relationship. This suggests that energy consumption is a highly reliable predictor of GWP, and vice versa, likely due to the direct relationship between energy production and the associated greenhouse gas emissions. In contrast, lighting levels and sound levels exhibited weak or statistically insignificant correlations with the other variables, as evidenced by the \(p\)-values exceeding 0.05. This suggests that any observed linear relationship between these factors and the others is likely attributable to random variation rather than a systematic association. The contrasting seasonal behavior underscores the complex interactions between these variables and highlights the necessity for strategic energy management throughout the year. In this context, a notable performance gap has emerged due to the complexity and fragmentation of building control system control. In this educational building, the strategies implemented to manage building services proved to be inadequately adaptive to the fluctuating demand levels. This was particularly evident during periods of partial occupancy or outside of the standard operating hours, leading to situations where the systems continued to heat unoccupied spaces. Such inefficiencies contributed to unnecessary energy consumption, exacerbating the environmental impacts outlined in the previous analysis. This disconnect not only highlights the need for better synchronization between energy management strategies and occupancy patterns but also emphasizes the importance of integrating intelligent control systems [4; 91].