无人机和卡车配送的碳排放比较评估

Reducing carbon emissions during the delivery process has become a research priority in logistics, and in this context, drones show great potential for last-mile delivery. However, few studies have compared the carbon emissions of trucks and drones. The calculation of carbon emissions from drones needs to take into account the main factors affecting energy consumption, such as the distance, quantity and weight of parcel delivery, but the existing studies seldom take into account the combined effects of the above factors.Based on this, this study establishes a carbon emission calculation model for drones and trucks . Further, the sensitivity analysis of important parameters is carried out through actual data experiments. The results of the study show that: ① When the weight and number of parcels are small and the delivery distance is farther, the use of drone delivery can get less carbon emissions. ② When the weight of parcels is smaller and the delivery distance is closer, the more the number of parcels, the more drone delivery should be chosen. ③ Drones are more sensitive to weight changes, and when the delivery distance and number of parcels are large, the larger the weight of the parcels, the more the truck has the advantage of carbon emissions. ④ Drones can better utilize their advantages in scenarios where the distribution distance is closer and the customer density is higher, while trucks are more suitable for scenarios where the distance is farther and the customer density is smaller, such as rural or remote areas. The model proposed in this paper is the basic model for the comparative study of carbon emissions from drones and trucks, which incorporates the weight, distribution distance, and number of parcels into the carbon emissions comparison model of drones and trucks at the same time, enriches the existing model study of carbon emissions calculation from drones, and provides decision-making suggestions for logistics enterprises to scientifically choose distribution methods to effectively reduce carbon emissions in different contexts.