# Airline Network Planning Considering Climate Impact: Assessing New Operational Improvements
## 4 Discussion  
We developed a multi-objective integrated network planning framework that can incorporate the climate impact and the commonly used profit in its objective function. ISO and FL OIs are also merged and were assessed by the developed framework to find the potential gains of considering them in the network planning phase. Four scenarios were investigated using the proposed framework. The BAU was assumed to represent the current airlines' situation and assumed to be the reference case for pairwise analysis. Profit is the only objective in the BAU scenario. In contrast, a transformed objective function was used in all other three scenarios to simulate the network decision-making process when the climate impact was also considered. We neglected the impact on passenger demand and prices in these studies and considered that the climate impact and profit have equal weights in the transformed objective function. The climate impact of a flight depends on the emission compounds, the location, and multiple other climatological parameters such as temperature, etc. As the proposed framework was developed to serve strategic studies, the climate impact was calculated using great cycle trajectories and the average climatological parameters. We conducted a comparison of our findings with the existing literature in Table 4, focusing on three key indicators: financial implications (profit loss), climate change mitigation (ATR100), and route efficiency (LF). Overall, we found that previous studies tend to overestimate the impact of the OIs. This discrepancy arises from the influence of network effects on the outcomes of adopting different OIs. Specifically, airlines face limitations in terms of the number of aircraft available for allocation within their network to exploit the benefits of these OIs while still efficiently meeting the overall demand. They must consider daily and weekly demand patterns and ensure smooth passenger connections at hub airports. These factors result in cascading effects on multiple routes, limiting the potential benefits of adopting the OIs. Furthermore, some of the OIs involve additional flight times and DOC, which can affect the feasibility of implementing these OIs on certain routes within the network. Our results reveal that, in several routes, the OIs are only adopted for a portion of the flights operating on those routes. The ISO and FL options are typically used when the fleet is available to fly for longer durations without compromising connection waves at the hubs. However, when such conditions are not feasible, the optimal flight decision is to choose the non-stop flight option at the standard altitude and speed. In summary, our analysis underscores the importance of considering network effects and operational constraints when evaluating the potential impact of OIs. These factorsinfluence the adoption of OIs on specific routes, and the optimal flight decision depends on the availability of the fleet and the need to maintain smooth connections at hub airports. The airline network level comparison between the ATR and BAU scenarios revealed a trade-off between profitability and climate impact. Long-haul flights are more profitable but also have a significantly higher climate impact. The ATR scenario showed that allocating aircraft to shorter routes was a common pattern for all three airlines to mitigate their climate impact. Although short-haul flight schedules were greatly impacted, long-haul flights experienced minimal changes in schedule and frequency. Not all long-haul flights saw a decrease in weekly frequency; the fleet was reallocated to other routes to maintain the average fleet utilisation and profit. If sufficient demand exists, some of the most profitable long-haul flights attract part of this unused aircraft capacity. Another option to avoid grounding the long-haul fleet in the ATR scenario is to serve medium-haul destinations.