noads 1.0

Numerical Optimization of Aviation Decarbonization Scenarios with GEMSEO-JAX

Numerical Optimization of Aviation Decarbonization Scenarios (NOADS)

Overview

Numerical Optimization of Aviation Decarbonization Scenarios with GEMSEO-JAX.

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.

Mitigation scenarios are formulated as optimization problems and three applications are demonstrated: no-policy baselines, single-policy optimization, and scenario-robust policies.

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.

NOADS accompanies the paper Numerical optimization of aviation decarbonization scenarios: balancing traffic and emissions with maturing energy carriers and aircraft technology (Costa-Alves et al., Applied Energy, 2026, doi:10.1016/j.apenergy.2026.127631). The documentation includes the paper as a browsable "extended paper" with the full supplementary information.

Citation

If you use NOADS in your research, please cite the paper:

@article{costaalves2026,
    title = {Numerical optimization of aviation decarbonization scenarios: balancing traffic and emissions with maturing energy carriers and aircraft technology},
    journal = {Applied Energy},
    volume = {412},
    pages = {127631},
    year = {2026},
    issn = {0306-2619},
    doi = {https://doi.org/10.1016/j.apenergy.2026.127631},
    url = {https://www.sciencedirect.com/science/article/pii/S0306261926002837},
    author = {Ian Costa-Alves and Nicolas Gourdain and François Gallard and Anne Gazaix and Yri Amandine Kambiri and Thierry Druot},
    keywords = {Multidisciplinary optimization, Low-carbon fuels, Aircraft design, Integrated assessment models, Shared socioeconomic pathways},
}

Installation

Install the latest version from PyPI (Python 3.10-3.12):

pip install noads

JAX runs on CPU by default, which is sufficient to reproduce all the scenarios.

Quick start

from noads.application.examples import single_policy_scenario_optimization

results = single_policy_scenario_optimization(
    global_scenario_name="SSP2-26",  # SSP2 pathway, 2°C target
    technology_index=1,               # mid-tech assumptions
    carbon_budget_percent=3,          # aviation's share of the global CO2 budget
    plot_optimum=True,
)

The documentation covers a 10-minute quickstart, the core concepts, a user guide, runnable example galleries for the elementary models and every scenario family of the paper, and the API reference. Pre-computed optima are shipped in docs/examples/optimization/results/ so the comparison examples run in seconds; the run_*.py scripts reproduce each optimization from scratch.

Documentation

The built documentation, including the "extended paper" and the API reference, is published at iancostalves.github.io/noads. The docs GitHub Actions workflow runs the test suite first (unit tests plus the documentation-deployability checks in tests/test_docs.py) and only builds and publishes the site, on pushes to main, once the tests pass.

To build it locally:

git clone https://github.com/iancostalves/noads.git
cd noads
pip install -e ".[doc]"
sphinx-build -b html docs docs/_build/html

Then open docs/_build/html/index.html in a browser. Equivalently, with tox:

tox -e doc          # one-off build into docs/_build/html
tox -e doc-serve     # live-reloading local server (sphinx-autobuild)

The first build re-executes the plot_*.py example scripts (a few minutes, loading the pre-computed optima shipped in the repo); -W --keep-going is used in CI to fail on any warning. run_*.py scripts (full scenario optimizations, hours of compute) are rendered without execution and are not part of the build.

Development

Clone the repository and install in editable mode with the development extras:

git clone https://github.com/iancostalves/noads.git
cd noads
pip install -e ".[test,doc]"

Common tasks (automated with tox, using tox-uv):

tox -e test         # run the test suite (or: pytest tests/)
tox -e check        # lint and format via pre-commit (ruff)
tox -e doc          # build the documentation with Sphinx into docs/_build/html
tox -e doc-serve    # serve the documentation with live reload
tox -e dist         # build and check the PyPI distribution
tox -e update-deps  # regenerate the frozen requirements files

The documentation is built with Sphinx (MyST markdown, sphinx-book-theme, sphinx-gallery). Example scripts under docs/examples/ follow the sphinx-gallery format: only plot_*.py scripts are executed during the build; run_*.py scripts (full optimizations) are rendered without execution.

Credits

The Generic Airplane Model (GAM) code was re-written in JAX together with Yri Amandine KAMBIRI. For more information on the GAM model please check Kambiri et al., Energy consumption of Aircraft with new propulsion systems and storage media, Scitech Forum, Orlando, January 2024.

Furthermore, open-access data are also used for calibration of air traffic demand (World Bank, ICAO) and for linking the background with a scenario from the IPCC's 6th Assessment Report (AR6 scenario database):

• Population, total, SP.POP.TOTL; GDP (current US$), NY.GDP.MKTP.CD; Air transport, registered carrier departures worldwide, IS.AIR.DPRT. World Development Indicators. World Bank Group Archives, Washington, D.C., United States.

• RPK. World Airlines Traffic and Capacity, Airlines for America. Source: ICAO. url: https://www.airlines.org/dataset/world-airlines-traffic-and-capacity/

• Primary Energy (Biomass); Final Energy (Electricity); Emissions Energy Supply (Electricity); GDP; Population. Variables are taken for a select scenario range, C1 (limit warming to 1.5°C with no or limited overshoot) to C6 (limit warming to 3°C), and can be vizualized here. Source: Byers et al, 2022. AR6 Scenarios Database hosted by IIASA. International Institute for Applied Systems Analysis, 2022. doi: 10.5281/zenodo.5886911 | url: data.ece.iiasa.ac.at/ar6/

Licences

Most of the repository is under the GNU Lesser General Public License v3 (LGPL3), same as GEMSEO and GAM.

However, some equations, such as sigmoid functions instead of custom time-dependent controls for aircraft penetration [noads.core.models.fleet.aircraft_operation] and quadratic load factor [noads.core.models.traffic] are a direct JAX re-implementation of AeroMAPS models, therefore they are left with GNU General Public License v3 (GPL3). The model instantiation [noads.application.base_objects], scenario setup [noads.application.scenario_setup.single_scenario_setup], scenario optimization examples [noads.application.examples] and the documentation that depend on these are also GPL3.

The examples are distributed under the BSD 0-Clause license and the documentation under CC BY-SA 4.0.

Bugs and questions

Please use the GitHub issue tracker to submit bugs or questions.

Contributing

See the contributing section of GEMSEO.

Contributors

• Ian COSTA ALVES
• François GALLARD
• Yri Amandine KAMBIRI