Numerical optimization of aviation decarbonization scenarios: balancing traffic and emissions with maturing energy carriers and aircraft technology

TL;DR

This work tackles aviation decarbonization by embedding detailed aircraft technology and energy-pathway modeling inside a Shared Socioeconomic Pathways framework, enabling end-to-end optimization of demand and supply levers.It introduces a multidisciplinary optimization approach (GAM energy model, energy pathways, and demand dynamics) with gradient-based methods to handle high dimensionality and scenario-robustness.Key findings show emissions peak around 2035–2040 under baseline trends, and Paris-aligned targets require either demand caps or additional low-carbon energy supply, with breakthrough aircraft offering the most potential under favorable conditions.The work demonstrates substantial computational gains using automatic differentiation (GEMSEO-JAX), reducing optimization time by orders of magnitude and enabling practical scenario analysis.Overall, the framework informs how technology choices, energy resource allocations, and demand management interact to shape aviation decarbonization trajectories.

Abstract

Despite being considered a hard-to-abate sector, aviation's emissions will play an important role in long-term climate mitigation of transportation. The introduction of low-carbon energy carriers and the deployment of new aircraft in the current fleet are modeled as technology-centered decarbonization policies, while supply constraints in targeted market segments are modeled as demand-side policies. Shared Socioeconomic Pathways (SSPs) are used to estimate trend-mitigation traffic demand and to limit the sectoral consumption of electricity and biomass. Mitigation scenarios are formulated as optimization problems, and three applications are demonstrated: no-policy baselines, single-policy optimization, and scenario-robust policies. Results show that the choice of energy carrier is highly dependent on assumptions regarding aircraft technology and the background energy system. Across all SSP-based scenarios, emissions peak by around 2040, but achieving alignment with the Paris Agreement requires either targeted demand management or additional low-carbon energy supply. The use of gradient-based optimization within a multidisciplinary framework enables the efficient resolution of these nonlinear, high-dimensional problems while reducing implementation effort.

Figures (11)

• Figure 1: Conceptual view of the optimization process and data-flow between modeled disciplines.
• Figure 2: Historical evolution and modeled future global RPK demand from socioeconomic drivers linked to SSP scenarios. Results are compared with franz_wide_2022. Explicitly incorporating per-capita demand saturation (current model) yields much lower traffic levels in the long term, when compared with models based on the extrapolation of short term income elasticities.
• Figure 3: Expected passenger efficiency (inverse energy consumption) of prospective aircraft with technology available by Entry-Into-Service. Color-code is used to differentiate among aircraft architectures, filled between the upper and lower limit for the technology scenarios, solid line shows lower technology scenario, dotted line shows mid technology scenario. The black dashed line shows the performance of the 2019 mean fleet within the considered distance-bands salgas_aeroscope.
• Figure 4: Comparison of traffic, annual emissions, and cumulative emissions of no-policy baseline scenarios SSP1 (dark green), SSP2 (blue), and SSP5 (purple). The sensibility to aircraft technology is displayed with 3 aircraft technology scenarios: Lower technology (continuous line), Mid technology (dotted line), and Upper technology (shadowed region).
• Figure 5: Comparison of drop-in (a) and breakthrough (b) mitigation scenarios under: trend, trend with extra energy availability, and low-demand formulations. The sensibility to aircraft technology is displayed with 3 scenarios of component-level performances: Lower technology (continuous line), Mid technology (dotted line), and Upper technology (shadowed region). These also impact the performance of new aircraft designs, driving the choice of which architectures to deploy and when they are launched.