Conclusion

Overall results show that, under trend demand growth: baseline scenarios display a peak in emissions between 2035 and 2040, mitigation scenarios based solely on SAF has limited emissions reductions due to energy availability constraints (2070 emissions are still 75 % of 2019 with preferential availability), breakthrough aircraft technologies can allow for reaching near zero emissions, but their impact is delayed to after 2045 due to late Entry-Into-Service and slow fleet renewal.

The choice of which aircraft architecture and energy carrier to embark is highly dependent on vehicle performance (determined by TLAR and technology assumptions) and on the background energy system (due to the timing of electricity decarbonization and to limited availability of electricity and biomass).

In order to respect Paris Agreement targets under an effort-sharing principle, drop-in mitigation will put the system to a higher stress: either by constraining traffic, or by consuming too much biomass and electricity. New aircraft with alternative energy carriers allows to reduce such stress by making better use of the same energy resources, even if their energy consumption is higher than that of conventional planes.

When comparing mitigation policies with different objective functions, it was found that supply caps, energy availability, and the introduction of alternative aircraft designs are complementary rather than competing measures. One strategy alone can achieve reduction in emissions up to a certain level, but their combined use is capable of more efficient mitigation by: using alternative aircraft in its feasible markets (reducing needs for low-carbon electricity and biomass for a given service), and avoiding emission-intense markets (further reducing consumption of fossil kerosene).

Regarding the numerical methods, the use of GEMSEO-JAX allowed for both reducing implementation burden and execution time for the optimization of mitigation scenarios. The simulation of these optimization-based policy scenarios was prohibitive without the speedups obtained, which are of two orders of magnitude at the scenario optimization level and three orders of magnitude at the scenario computation and linearization level.

Our research offers practical insights on how to efficiently use optimization algorithms for mitigation scenarios. Applying these methods for the aviation sector, also shed light on how to optimally allocate aircraft architectures, energy resources, and demand-side measures to achieve stringent mitigation targets.