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 a technology-centered decarbonization policy, and supply constraints in targeted market segments are modeled as demand-side policy. Shared socioeconomic pathways (SSP) are used to estimate the trend traffic demand and limit the sectoral consumption of electricity and biomass. Mitigation scenarios are formulated as optimization problems and three applications are demonstrated: single-policy optimization, scenario-robust policy, and multiobjective policy trade-off. Overall, we find that the choice of energy carrier to embark is highly dependent on assumptions regarding aircraft technology and background energy system, and that aligning trend scenarios with the Paris Agreement market-targeted traffic constraints are required to align trend scenarios with the Paris Agreement. The usual burdens associated with nonlinear optimization with high-dimensional variables are dealt with by jointly using libraries for Multidisciplinary Optimization (GEMSEO) and Automatic Differentiation (JAX), which resulted in speedups of two orders of magnitude at the optimization level, while reducing associated implementation efforts.

翻译：尽管被视为难以减排的领域，航空排放仍将在交通领域的长期气候减缓中发挥重要作用。本文将低碳能源载体的引入及新飞机在当前机队中的部署建模为以技术为中心的脱碳政策，并将目标市场细分中的供应约束建模为需求侧政策。采用共享社会经济路径（SSP）估算趋势交通需求，并限制电力与生物质的部门消耗。减缓情景被构建为优化问题，并展示了三种应用：单一政策优化、情景鲁棒性政策及多目标政策权衡。总体而言，我们发现能源载体的选择高度依赖于飞机技术及背景能源系统的假设，且需将趋势情景与《巴黎协定》市场导向的交通约束相协调，才能使趋势情景符合《巴黎协定》目标。针对高维变量非线性优化通常带来的计算负担，本文通过联合使用多学科优化库（GEMSEO）与自动微分库（JAX）进行处理，在优化层面实现了两个数量级的加速，同时降低了相关实施工作量。