Now we turn our attention to sustainable vehicle routing problems (VRPs). VRPs that have additional attention on energy consumption are called green VRPs (GVRPs) [
24,
25]. A comparative study of the GVRP literature can be found in Poonthalir and Nadarajan [
26]. GVRPs aim to optimize routes and they also consider environmental issues and related financial costs. Similarly, pollution routing problems (PRPs) consider a vehicle speed as a decision variable to reduce pollutant emissions [
27]. Lin et al. [
28] summarized various types of problems and their variants in the field of GVRPs and PRPs, and Chiang et al. [
25] outlined the research trend of GVRPs. GVRPs usually set the objective to reduce the total travelling distance of vehicles since it is known that CO
2 emissions and fuel consumption are proportional to the travelling distance. Besides the travelling distance, various factors are known to be influential on fuel consumptions [
29]. A methodology for calculating transport emissions and energy consumption (MEET) [
30] was provided using various factors, such as vehicle miles traveled, vehicle speed, load weight, and road gradient. Naderipour and Alinaghian [
31] presented a comprehensive model modified to calculate pollution emissions more precisely in VRPs. As research on GVRP progresses, various factors including the travelling distance are considered in mathematical models. Kara et al. [
32] presented an energy maximizing VRP (EMVRP), a variant of capacitated VRP (CVRP), which considers the load of a vehicle as well as the travelling distance. Later, the speed of vehicle was considered by Kuo [
33] and Kuo and Wang [
34] included the payload weight in addition to the speed. The speed of vehicle is also dependent on the degree of congestion of a route and, thus, traffic congestion was considered in their mathematical model. This type of problems is called time-dependent VRP (TDVRP). The vehicle speed in a TDVRP is not constant, but dependent on the departure time [
35]. An emission-based TDVRP is called E-TDVRP, which includes travel time, fuel, and CO
2 emissions [
36]. Jabali et al. [
36] presented a framework to model CO
2 emissions in a TDVRP. Zhou and Lee [
37] developed a TDVRP to minimize greenhouse gas emissions. A variety of factors such as three-dimensional customer locations, gravity, vehicle speed, vehicle operating time, vehicle capacity, rolling resistance, air density, road grade, and inertia were considered to estimate greenhouse gas emissions. Recently, Shen et al. [
38] proposed a TDVRP that considers the driver’s salary and penalty costs in addition to fuel costs and carbon emission trading costs. Shen et al. [
38] named it a low-carbon multi-depot open vehicle routing problem with time windows (MDOVRPTW). Open VRP has two kinds of vehicles, as follows: Company-owned and rental. Reverse logistics (the definition can be found in Dekker et al. [
39]) was also considered as a kind of GVRP. However, a reverse logistics problem that considers pollution emissions has not been studied yet. Even though an approximation of the average energy and battery costs per kilometer for drones was proposed by D’Andrea [
40], there is little research about the impact of UAVs on the environment. A possible reason is that UAVs have not yet been actively deployed in the delivery industry. As another field of VRP research that studies the environment, there are VRPs using alternative fuel vehicles (e.g., electric vehicles). Schneider et al. [
41] and Lin et al [
42] presented an electric VRP (EVRP). A VRP that uses a mixed fleet of electric vehicles and conventional (internal combustion engine) commercial vehicles was considered by Goeke & Schneider [
43]. Recently, Macrina et al. [
44] presented a GVRP with mixed vehicle fleets.
Recently, there have been studies on comparing the sustainability of UAVs with that of other transport, e.g., GVs. Goodchild and Toy [
45] made a large-scale experiment considering real-world operations in Los Angeles region, and found the advantages of adopting UAVs in terms of reducing CO
2 emissions. However, they did not address the VRP issues, such as generating optimal routes. Coelho et al. [
46] suggested a multi-objective green UAV routing problem. The total travelling distance, total delivering time, number of UAVs used, maximum speed of UAVs, makespan, and total amount of necessary energy were simultaneously optimized based on the multi-objective optimization model. However, estimation of CO
2 emissions was not taken into account. Chiang et al. [
25] compared a GV-alone system and a GV-along-with-UAV system in terms of CO
2 emissions and related costs when delivering goods to customers. Dukkanci et al. [
47] presented a drone delivery problem that minimizes the total operational costs, which includes energy consumption during the delivery. Their system uses traditional delivery vehicles (GVs) as launch points for drones, which is also a GV-along-with-UAV system. Park et al. [
48] compared drone and motorcycle deliveries of pizzas in rural and urban areas in terms of greenhouse gas emissions and exhausted particulates. The drone delivery as well as the motorcycle delivery have one destination from the departure point and, thus, neither multi-hopping of drones nor consideration of the travel route for both drones and motorbikes is necessary. Our work compares UAV-alone and GV-alone delivery systems to serve the same set of customers. We take into account the optimal delivery routes of each system, which reflects each system’s characteristics, such as a maximum weight to carry and a maximum trip travelling distance. The comparison is performed by means of CO
2 emissions in each system’s optimal delivery scheme, which includes delivery routes and vehicle (GV or UAV) operations. Using this approach, we can directly and quantitatively compare two different logistic systems.