  The adoption of Uncrewed Aerial Vehicles (UAVs) for last-mile deliveries will
affect the energy productivity of package delivery and require new methods to
understand the associated energy consumption and greenhouse gas (GHG)
emissions. Here we combine empirical testing of 187 quadcopter flights with
first principles analysis to develop a usable energy model for drone package
delivery. We develop a machine-learning algorithm to assess energy use across
three different flight regimes: takeoff, cruise, and landing. Our model shows
that, in the US, a small electric quadcopter drone with a payload of 1 kg would
consume approximately 0.05 MJ/km and result in 41 g of CO$_{2}$e per package.
The energy per package delivered by drones (0.19 MJ/package) can be up to 96\%
lower than conventional transportation modes. Our open model and generalizable
coefficients can assist stakeholders in understanding and improving the energy
use of drone package delivery.
