## 6 Computational Results and Discussion
### \(Co_{2}\) Results Comparison  
In order to ensure the realism of our results, we have carefully calculated the fuel consumption, taking into account the technical specifications of one of the most widely used commercial vehicles for last-mile transport, namely, the Mercedes-Benz Sprinter Cargo 311 CDI kompakt van. This van has the same engine as its counterpart for passenger transport. Based on the available technical data, the Mercedes-Benz Sprinter Cargo van has fuel consumption and \(\text{CO}_{2}\) emissions of 9.6 1/100 km and 247 \(\text{gCO}_{2}\)/km, respectively. For more details on the official fuel consumption and specific \(\text{CO}_{2}\) emissions, refer to [63]. For calculating the fuel consumption of private cars, we have been guided by the average consumption of regular gasoline and diesel in Germany (with a split of about two thirds regular and one third diesel), which results in about 7.5 1/100 km on average [64; 65]. As far as emissions are concerned, the value is based on a baseline of 178 \(\text{gCO}_{2}\)/km [66]. Building upon the previous section, we conduct an evaluation of each APL configuration. This comprehensive assessment is rooted in the technical data related to both fuel consumption and emissions, and it extends across the four specified demand scenarios H, S1, S2, and S3. The evaluation of \(\text{CO}_{2}\) emissions for the APL configurations has been conducted for the four demand scenarios, with detailed results provided in Appendix A (Table A3). Figure 15 offers a comparison of \(\text{CO}_{2}\) metrics for APL systems for private vehicles and DD, with a constant parameter value of C = 250. Our analysis indicates a consistent trend regarding \(\text{CO}_{2}\) emissions. In all scenarios, the A1 configuration, featuring 25 APIs, exhibits the highest emissions. When assessing the A1 APIs configuration, it becomes evident that DD demonstrates significant efficiency gains, with improvements of 334%, 375%, 399%, and 422% for the Hermes, S1, S2, and S3 scenarios, respectively. Across configurations A2, A3, A4, and A5, DD consistently results in lower \(\text{CO}_{2}\) emissions compared to using APIs in conjunction with private vehicles for parcel pickup. However, it is noteworthy that only in the A6 configuration is APL usage more efficient, with a maximum advantage of 4%. Figure 16 presents a comparison of \(\text{CO}_{2}\) metrics for APL systems, maintaining a value of C = 500 given in Appendix A (Table A4). The observed trends remain consistent for APL scenarios A2, A3, and A4. Notably, it becomes evident that in scenarios A5 and A6, the advantage of using APIs over DD ranges between 13% and 22%. In comparing APLs and DD in terms of \(\text{CO}_{2}\) emissions, the results, considering customer behavior, offer a comprehensive assessment. APL systems have operational cost advantages, but to keep emissions comparable to DD, the city of Dortmund needs a minimum of 250 APIs, as per the A5 and A6 scenario results. In the last experiment, a sensitivity analysis has been conducted. For three different parameters (_ratio foot or bike_, _ratio public transport_, and _ratio individual tour_) from Table 1, an iterative increase in the parameter value (with an increment of one percentage point)  
Figure 14: Comparison of distances traveled by customers. is analyzed until a better result (in terms of CO2 emissions) than for direct deliveries is achieved. In Figure 17, the results for increasing the percentage of customers (with a distance between 0.3 and 1.5 km to the nearest APL) picking up their parcels by foot or bike are given. For scenario A1, no superior results can be obtained, even if the respective parameter is set to 99% (as indicated in the figure). For all other APL configurations, a parameter setting can be determined where the APL system leads to fewer CO2 emissions than DD. The more APLs that are introduced and the higher the capacity of the vehicle serving the APLs, the lower the respective break-even point for the considered parameter _ratio by foot or bike_ is.