Replication package: Flight Authorization Limits Emissions Abatement in Truck--Drone Last-Mile Delivery

Code and results supporting the article of the same name, on how airspace flight authorization limits the emissions abatement achievable by hybrid truck-drone last-mile delivery. The code implements a multi-objective truck-UAV routing model in which emissions are an output of the routing decision rather than an assumption about it: a momentum-theory energy model with payload dependence in place of a distance proxy, well-to-wheel emissions accounting, an explicit truck-only counterfactual solved on the same instances, and SORA-derived authorization limits entering as arc eligibility. Twelve operating scenarios vary the authorization ceiling, population density, and whether a sheltering mitigation is claimed, repeated across five independently generated service areas. Solutions come from NSGA-II and MOPSO over a shared random-key encoding, validated against proven optima from a mixed-integer program on small instances. CONTENTS. code/ holds the model package (tdrp/) and the scripts that produce every reported result: run_multigeo.py for the main experiment, run_evan.py for the electric-van counterfactual, seed_scaling.py for the measurement-resolution diagnostic, run_validation.py and test_validation.py for validation against exact optima, and make_figs_multigeo.py, make_figs_converged.py and make_fronts.py for the figures. results/ holds the result files behind every table and figure. ASSUMPTIONS_REGISTER.md documents each modelling choice and its provenance. The reported emissions results are in results/results_multigeo_50seed.json: five service areas, twelve scenarios each, 2000 generations, 50 seeds, with the truck-only counterfactual solved once per seed and selected on minimum cost. Reproduction requires Python 3.10 or newer with NumPy pinned to 2.4.4; NumPy gives no guarantee that its random streams are stable across versions, so a different version changes every seeded run. code/HOW_TO_RUN.md gives step-by-step commands, the expected output at each stage, and the values to check against. Instances are determined entirely by customer count, scenario and random seed, so any result regenerates exactly on a matching environment.