# Economic Impact Analysis of the Application of Different
## 4 Results of Economic Evaluation on Selected Alternatives of Model Sections
### Parameter of Internal Rate of Return (IRR) during the Pavement Repair Life Cycle for Model A  
As shown in previous sections, road degradation expressed with the \(RUT\) parameter can produce different benefits and resulting cash flows. The cash flow as a result of CBA can be expressed in economic terms by economic indicators. The key economic indicator we used is \(IRR\), as it provides a good perspective on potential future cash flows. Figure 7 shows the dependency of \(IRR\) on the \(RUT\) parameter. The \(RUT\) value means the \(RUT\) parameter value at the time of repair. The \(IRR\) value shows how the economic result of such repair after 10 years of cash flows is calculated as described in previous sections. For instance, if we repair the pavement at \(RUT=6\) mm, we use pavement deterioration model related to Equation (2), we calculate cash flows for 10 years of operation of the repaired pavement--the resulting \(IRR\) would be \(-19.3\%\). Figure 8 shows that performing the repair too early, will lead to economically inefficient repair. It is worth noting that, in practice, an \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Gradient [\%]**} & \multirow{2}{*}{**Curvature [deg/km]**} & \multirow{2}{*}{\(RUT\) [mm]**} & \multirow{2}{*}{**Fuel**} & \multirow{2}{*}{**Oil**} & \multirow{2}{*}{**Travel Time**} & \multirow{2}{*}{**Tires**} & \multirow{2}{*}{**Spare Parts**} & \multirow{2}{*}{**Maintenance**} \\  & & & & & & & & \\ \hline \multirow{3}{*}{0} & \multirow{3}{*}{0} & \(<\)5 & 1 & 1 & 1 & 1 & 1 \\  & & 10 & 1.02 & 1.33 & 1.08 & 1.46 & 1.98 & 1.45 \\  & & 25 & 1.09 & 1.67 & 1.27 & 1.92 & 3.93 & 2.15 \\ \hline \multicolumn{10}{c}{**Heavy Lorry**} \\ \hline \multirow{2}{*}{**Gradient [\%]**} & \multirow{2}{*}{\(\begin{array}{c}\text{{\bf Curvature}}\\ \text{\bf[deg/km]}\end{array}\)**} & \multirow{2}{*}{\(RUT\) [mm]**} & \multirow{2}{*}{**Fuel**} & \multirow{2}{*}{**Oil**} & \multirow{2}{*}{**Travel Time**} & \multirow{2}{*}{**Tires**} & \multirow{2}{*}{**Spare Parts**} & \multirow{2}{*}{**Maintenance**} \\  & & & & & & & \\ \hline \multirow{3}{*}{0} & \multirow{3}{*}{0} & \(<\)5 & 1 & 1 & 1 & 1 & 1 \\  & & 10 & 1.03 & 1.11 & 1.22 & 1.04 & 1.56 & 1.26 \\ \cline{1-1}  & & 20 & 1.14 & 1.22 & 1.81 & 1.08 & 2.12 & 1.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Indexing of increase in resource consumption. earlier repair would also require cheaper repair technology, but that was not the focus of this analysis. Repair costs were fixed, as we aimed to study the impact of the deterioration model as the single variable and adhered to \"the ceteris paribus principle\". The impact of different performance models used for the calculation of cash flows and subsequent \(IRR\) calculation is very high. Using the function related to Equation (3) for repair at the end of the pavement life cycle (\(RUT=25\) mm) produces an \(IRR\) of 35%, whereas using the function related to Equation (1) produces an \(IRR\) of 25.5%. This leaves a 9.5% interval of uncertainty that can influence road administration decision making. These differences are also shown in Figure 8.  
Based on Figure 7, we can derive functions to describe the correlation between the \(rut\) parameter at which the repair action is performed and economic effectiveness, i.e., \(IRR\) indicator. Each of the derived functions corresponds to the equation described in Section 2 that was used in the LCCA for which the \(IRR\) was calculated. The respective equations are as follows:  
The correlation between \(IRR\) and \(rut\) parameter evolution following Equation (1):  
\[IRR=9.10^{-5}\;rut^{3}-0.0046\;rut^{2}+0.0991\;rut-0.7095 \tag{11}\]  
Figure 5: User benefits from the implementation of the repair model A.  
Figure 6: User benefits from the implementation of the repair model B.