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Life Cycle Greenhouse Gas Emissions for Last-Mile Parcel Delivery by Automated Vehicles and Robots.

Luyao LiXiaoyi HeGregory A KeoleianHyung Chul KimRobert D De KleineTimothy J WallingtonNicholas J Kemp
Published in: Environmental science & technology (2021)
Increased E-commerce and demand for contactless delivery during the COVID-19 pandemic have fueled interest in robotic package delivery. We evaluate life cycle greenhouse gas (GHG) emissions for automated suburban ground delivery systems consisting of a vehicle (last-mile) and a robot (final-50-feet). Small and large cargo vans (125 and 350 cubic feet; V125 and V350) with an internal combustion engine (ICEV) and battery electric (BEV) powertrains were assessed for three delivery scenarios: (i) conventional, human-driven vehicle with human delivery; (ii) partially automated, human-driven vehicle with robot delivery; and (iii) fully automated, connected automated vehicle (CAV) with robot delivery. The robot's contribution to life cycle GHG emissions is small (2-6%). Compared to the conventional scenario, full automation results in similar GHG emissions for the V350-ICEV but 10% higher for the V125-BEV. Conventional delivery with a V125-BEV provides the lowest GHG emissions, 167 g CO2e/package, while partially automated delivery with a V350-ICEV generates the most at 486 g CO2e/package. Fuel economy and delivery density are key parameters, and electrification of the vehicle and carbon intensity of the electricity have a large impact. CAV power requirements and efficiency benefits largely offset each other, and automation has a moderate impact on life cycle GHG emissions.
Keyphrases
  • life cycle
  • machine learning
  • high throughput
  • deep learning
  • endothelial cells
  • municipal solid waste
  • risk assessment
  • mass spectrometry
  • climate change
  • robot assisted
  • particulate matter