# Environmental Impacts of Cement Production: A Statistical Analysis
## 3 Results
### Other Emission Variables
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**ICs**} & \multicolumn{3}{c}{**Linear Regression (AP vs. Consumption ICs) CEM IV**} \\ \cline{2-4}  & **Coefficients** & **St. Dev.** & \(p\)**-Value** \\ \hline ADPe & 0.148 & 0.075 & * \\ ADPf & 0.375 & 0.080 & *** \\ TRPE & \(-\)0.179 & 0.053 & ** \\ SRM & \(-\)0.162 & 0.053 & ** \\ Water & 0.500 & 0.97 & \\ \hline \multirow{2}{*}{**ICs**} & \multicolumn{3}{c}{**Linear Regression (EP vs. Consumption ICs) CEM IV**} \\ \cline{2-4}  & **Coefficients** & **St. Dev.** & \(p\)**-Value** \\ \hline ADPe & 0.280 & 0.071 & *** \\ ADPf & 1.009 & 0.147 & *** \\ RSF & 0.254 & 0.122 & * \\ Water & \(-\)0.132 & 0.090 & \\ El & \(-\)0.122 & 0.087 & \\ \hline \multirow{2}{*}{**ICs**} & \multicolumn{3}{c}{**Linear Regression (POCP vs. Consumption ICs) CEM IV**} \\ \cline{2-4}  & **Coefficients** & **St. Dev.** & \(p\)**-Value** \\ \hline TRPE & \(-\)0.178 & 0.065 & ** \\ NRSF & \(-\)0.233 & 0.081 & ** \\ Water & 0.721 & 0.092 & *** \\ \hline \multirow{2}{*}{**ICs**} & \multicolumn{3}{c}{**Linear Regression (P vs. Consumption ICs) CEM IV**} \\ \cline{2-4}  & **Coefficients** & **St. Dev.** & \(p\)**-Value** \\ \hline ADPe & 0.314 & 0.107 & ** \\ ADPf & 1.504 & 0.272 & *** \\ NRSF & 0.411 & 0.172 & * \\ RSF & 0.458 & 0.180 & * \\ Water & \(-\)0.557 & 0.134 & *** \\ \hline \hline \end{tabular}
\end{table} 
Table 23: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**ICs**} & \multicolumn{3}{c}{**Linear Regression (AP vs. Consumption ICs) CEM II**} \\ \cline{2-4}  & **Coefficients** & **St. Dev.** & \(p\)**-Value** \\ \hline ADPf & 0.313 & 0.083 & *** \\ SRM & 0.486 & 0.292 & \\ Water & 0.778 & 0.070 & *** \\ El & \(-\)0.268 & 0.118 & * \\ \hline \multirow{2}{*}{**ICs**} & \multicolumn{3}{c}{**Linear Regression (P vs. Consumption ICs) CEM II**} \\ \cline{2-4}  & **Coefficients** & **St. Dev.** & \(p\)**-Value** \\ \hline RSF & \(-\)0.225 & 0.078 & ** \\ Water & 0.120 & 0.078 & \\ \hline \hline \end{tabular}
\end{table} 
Table 24: AIC selection regression summary—CEM IV. In answer to Question 3, the results in Table 25 highlight that, while in general using the same subset of Consumption IC relevant to predict GWP does not improve the accuracy of the linear model, performing separately for each Emission IC a variable selection procedure leads to a meaningful enhancement in terms of predictive assessment of the model. In Figure 8, all the important variables related to each Emission IC listed in Tables 19-21 are represented proportionally to their size. Figure 8 confirms that ADPf plays a pivotal role for the majority of IC emissions, that is, AP, EP, POCP and P (CEM I and CEM IV). CEM II differs from CEM I and CEM IV due to its limestone-based \begin{table}
\begin{tabular}{c c c} \hline
**IC** & **Model** & **RMSE** \\ \hline \multirow{3}{*}{AP} & linear & 0.54 \\  & AIC (GWP variables) & 0.53 \\  & AIC & 0.44 \\ \hline \multirow{3}{*}{EP} & linear & 0.59 \\  & AIC (GWP variables) & 0.61 \\  & AIC & 0.56 \\ \hline \multirow{3}{*}{POCP} & linear & 0.61 \\  & AIC (GWP variables) & 0.67 \\  & AIC & 0.54 \\ \hline \multirow{3}{*}{P} & linear & 0.73 \\  & AIC (GWP variables) & 0.75 \\ \cline{1-1}  & AIC & 0.71 \\ \hline \end{tabular}
\end{table} 
Table 25: Comparison of RMSE between the two models. Figure 8: Size of the coefficients (AIC selected components) for each class and for all the remaining Emission ICs. (absolute values). composition; particularly, POCP CEM II has its highest correlation with the Water consumption IC. It is confirmed by the upstream processes necessary to quarry natural raw materials.