{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/numerical-optimization-of-aviation","title":"Numerical optimization of aviation decarbonization scenarios: balancing traffic and emissions with maturing energy carriers and aircraft technology","arxiv_id":"2503.22435","date":"2025-03-28","proceeding":null,"authors":["Ian Costa-Alves","Nicolas Gourdain","François Gallard","Anne Gazaix","Yri Amandine Kambiri","Thierry Druot"],"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.","url_abs":"https://arxiv.org/abs/2503.22435v1","url_pdf":"https://arxiv.org/pdf/2503.22435v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"numerical-optimization-of-aviation","repo_url":"https://gitlab.com/ian.costa-alves1/noads","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"jax","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}