## 6 Discussion, Implications, and Limitations
### Limitations and Further Research Directions  
As with every research article, there are limitations to this study which create opportunities for further research. The main limitation of this study is the requirement of using only one decarbonization measure to validate the proposed method. The primary objective for future research is to evaluate the proposition for additional decarbonization strategies and suggest the most effective measures for each of the nine inventory categories. A large volume of literature is available that recommends and categorizes decarbonization techniques (see, e.g., [20] for a comprehensive list), which can be a useful starting point for future investigations. For example, a frequently discussed measure is the use of trucks with alternative drives. It would be interesting to investigate effective application scenarios and the differences in abatement costs among ABC/XYZ items. To test for these differences, we suggest collecting real-world data from case studies that have implemented GHG mitigation measures for a broad range of inventory items or conducting simulation-based studies. The first approach may be criticized for its comparability, while the second may be questioned for its validity. Nevertheless, data availability is a crucial issue for all approaches, which is why it is recommended that research institutions collaborate closely with industrial companies to obtain realistic data.  
Due to missing data, we neglect costs for damaged or stolen cargo. These have been modelled in other studies [58] and might harm CRRT's competitiveness. Although prior research indicates that CRRT is cost-competitive only when the pre- and post-haul distances are less than 40 km and the primary rail haul is no less than 750 km [75], the scenarios evaluated in this simulation are beneficial in terms of cost. Further, we have not included external costs in our study, as these do not currently have to be paid. However, a recent European study shows how dramatically different the results could be if external costs had to be paid in transportation [75].  
Regarding CRRT, many different parameters can be adjusted, and many different replenishment, order, dispatching, and routing strategies can be followed. As intermodal transportation is a highly complex system, we were not able to fully test all parameters but had to make some assumptions regarding how CRRT could be conducted. Although these assumptions were discussed with other researchers and practitioners, future studies might assess the impact of a change in load unit compositions, route selection, and many more operational aspects of CRRT.  
We were not able to validate the calculated figures for the mean and median of the train delay for Austria due to an absence of data. An analysis of train delays for intermodal freight services departing in Luxembourg resulted in \(E=74.35\) min and \(m=12\) min [79]. Another study found the standard deviation of freight train delays on one Swedish railway line to be \(\sigma=64.19\) min [85]. Assuming the underlying data is subject to an exponential distribution means that \(E=\sigma\). Comparing our computed results, \(E=39.51\) min and \(m=27.39\) min, and with these empirical data, we see that our approximation produces smaller delay times. On the other side, an older study utilizes an average anticipated delay time of \(D=30\) min [62]. To address the ambivalence in the results, the suitability of the exponential distribution should be further researched in future studies. During the literature review on train delay times, we found that the majority of studies dealt with real-time delay prediction for passenger trains, but almost no studies presented high-level metrics for freight trains [86]. Nevertheless, for simulations like ours and logistics network planning for shippers and logistics service providers, further research on this topic could be helpful.