# The Environmental Impact of Inland Empty Container Movements Within Two-Depot Systems
## 5 Results and Discussion  
The sensitivity analysis conducted via Behavior Space confirmed the robustness of these trends across a range of demand frequencies and routing strategies. By comparing the two operational scenarios with inland depot coordination (inland-depot? = true) and without depot usage (inland-depot? = false), the simulation highlights key differences in system efficiency, emissions, and service quality. The results show that container availability, customer waiting time, and CO2 emissions are highly related to the customer demand frequency. Subsequently, Figure 3 shows the variation in the number of empty containers over time (in days) based on different demand frequency levels (from 1 to 6) within the model. To maintain fair comparisons across two different scenarios, a demand frequency of 3 days is chosen as a baseline for further analysis. The selection of a 3-day customer demand frequency is grounded in a balance between system stability and analytical clarity. Through simulation, it was observed that shorter demand intervals (such as 1 or 2 days) led to higher system stress, characterized by sharp fluctuations in empty container availability and increased CO2 emissions due to frequent movements. Conversely, longer intervals (such as 5 or 6 days) resulted in underutilization of resources and stagnant system dynamics, where container flows and emissions remained relatively flat and less informative for analysis. Moreover, the selected demand frequency of 3 days as an optimal point can produce enough activity to highlight inefficiencies and potential improvements while keeping the system within operational realism. These findings validate the model's capacity to support logistics decision-making under dynamic, real-world conditions and demonstrate its added value over conventional practices. Figure 4 illustrates the cumulative GHG emissions in tons over time under two different scenarios. Both scenarios show steadily increasing total GHG emissions over time, following a nearly linear growth pattern. When inland depots are used, which is represented by the orange line, emissions increase at a slightly lower rate, resulting in approximately 1000-1500 tons less CO2 emissions compared to the direct routing system. This scenario can reduce the total CO2 emissions by approximately 15-25% compared to direct customer routing. This is due to the better consolidation of container movements, reducing unnecessary trips. However, if trucks move directly to customer locations without utilizing the depot, emissions accumulate at a faster rate, resulting in a 20-30% increase in emissions due to increased trips and empty backhauls. Figure 3: Customer demand frequency levels. Figure 5 demonstrates the number of empty containers over time under two different scenarios. When the inland depot is utilized, the number of empty containers stabilizes at a consistently lower level. The inland depot functions as a buffer, allowing for better coordination and redistribution of empty containers, which reduces the peaks and troughs seen in the other scenario. It leads to a reduction in empty container accumulation, implying that containers are being utilized more efficiently rather than sitting idle in depots or ports. This indicates a more balanced and efficient system where empty containers are allocated in a way that aligns better with demand patterns, whereas the number of empty containers in the direct strategy exhibits more pronounced fluctuations, with sharp drops and recoveries throughout the simulated period. This suggests inefficiencies in container circulation can be addressed to reduce bottlenecks in the supply chain. Figure 6 measures the average customer waiting time over the simulation period for both scenarios. The relationship between customer waiting time, unmet demand levels, and the configuration of the inland depot is very significant. Moreover, the results show that when an inland depot is used, customer waiting time increases exponentially, suggesting severe delays.