Drone flight data reveal energy and greenhouse gas emissions savings for small package delivery

TL;DR

This study combines empirical drone flight data and modeling to show that small electric drones can significantly reduce energy use and greenhouse gas emissions in last-mile package delivery compared to traditional methods.

Contribution

It introduces a new energy consumption model for drone delivery based on empirical data and machine learning, aiding stakeholders in assessing environmental impacts.

Findings

Drone energy use is approximately 0.05 MJ/km for a 1 kg payload.

Drones can reduce energy per package by up to 96% compared to conventional transport.

The model estimates 41 g CO2e emissions per package for drone delivery.

Abstract

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.