# Airline Network Planning Considering Climate Impact: Assessing New Operational Improvements
It should be noted that the previous research related to these two OIs either considered a single-flight mission or assumed that airlines' fleets, networks, and flight schedules would remain the same after implementing these OIs. The reported increase in flight time and costs may affect the planning decisions of airlines, resulting in different network and fleet allocations, and compromising the assessment of the impact of OIs. Such assessments can only be performed using airline network planning models, including network decisions. Many airline network planning studies are present in the literature, considering both fleet planning [26; 27] and network development [28; 29] to achieve fuel and climate cost minimisation.On the other hand, considering climate impacts while solving the network planning problem has not been fully explored. The work in [14] developed fuel and climate cost minimisation networks before optimising the flight trajectories of the aircraft operating the flights in both networks. There are few details about how the airline network and passenger flows were modelled. Nonetheless, the results suggest that only direct passengers were considered, and the flight frequencies per route were obtained. The study from [30] integrated the fuel and emissions per potential route in the network to optimise the airline network while considering both the profit and ATR. The authors used a mixed integer linear programming model (MILP) to optimise the network. Origin-destination marketswere used as inputs. However, passenger connections and interdependencies between the network planning and flight schedules were not directly modelled. The network changes when considering the temperature changes were limited to 1% of the available seat kilometres flown in a reference profit-based network to guarantee the stability of the solutions. Furthermore, previous studies [14,30] disregarded the impact on the airline network when adopting OIs. OIs, such as ISOs and flying low, increase the flight times and reduce the fleet availability to operate current networks. In addition, passenger connections at hub airports can be compromised by new flight times. In this paper, we took a step forward by integrating a multi-objective network optimisation model with the previously mentioned OIs to simulate the decision process of a climate-aware airline network and its implications on the flight schedule. The developed framework helps identify the modifications necessary to adopt sustainable network planning and OIs by providing a detailed network model, flight schedule, aircraft rotations, and passengers' itineraries. Eventually, the potential climate impact mitigation is calculated for climate-aware network design and network planning including the OI combination. An extensive range of climate and non-climate key performance indicators (KPIs) are utilised to assess the implications across various scenarios and case studies. The climate KPIs include emissions from the flights (CO\({}_{2}\) and non-CO\({}_{2}\)) and ATR values. These KPIs are calculated for the entire network and are further categorised into short-haul and long-haul flights, providing a more comprehensive understanding of the studied OI's impact. Non-climate KPIs consist of monetary and operational performance indicators such as pax, load factor (LF), etc. An iterative approach was adopted to generate the network and flight schedule from scratch. Starting from scratch ensures that the current network and flight schedule characteristics do not influence the final result. The pairwise comparison of the results illustrates how adopting a mitigation strategy can help reduce climate impacts. We also show that the more significant mitigation of climate impact could be achieved by combining OIs. This was captured by comparing the scenarios associated with implementing the OIs with a reference scenario in which no OIs are included. The primary contributions of the present study are as follows:  
1.