According to the International Energy Agency (IEA) transportation is responsible for 24% of global greenhouse gas (GHG) emissions due to fuel combustion, causing 8.2 Gt CO-eq. in the year 2018. Passenger road vehicles (cars, buses, two-wheelers) account for 44% of those emissions, which continue to rise slightly, despite efficiency gains in vehicle technology, due to rising traffic volumes. Another reason is the increasing number of larger vehicles sold, which not only have a high energy demand per passenger transported, but additionally occupy a lot of space in already crowded cities [ 1 ]. Light electric vehicles (LEV), as a part of micromobility, offer numerous advantages for urban mobility over conventional vehicles with internal combustion engines. For example, LEVs require less energy for production and operation as well as less space than cars [ 2 ]. Thus, micromobility and new ownership models such as sharing services with LEVs are emerging in cities worldwide [ 3 4 ].
Due to the dynamic market development for micromobility, the corresponding terms, its business models and associated transport modes have not yet been clearly defined. The following terms are used in this paper: shared mobility describes “the shared use of a vehicle [...] that enables users to have short-term access to transportation modes on an ‘as-needed’ basis [ 5 ]”.
Micromobility is defined as the use of low speed, small, lightweight vehicles with a mass of less than 350 kg and a design speed up to 45 km/h [ 6 ]. The vehicles are typically electric powered [ 7 ]. They require battery capacities from 0.4 kWh to 10 kWh, resulting in drive ranges of 20–160 km [ 8 ]. LEV is often used synonymously, covering different types of vehicles in the field of micromobility, such as electric stand-up scooters, driven in a standing position; electric bicycles; Electric Moped scooters; or light four wheeled vehicles [ 2 ].
3 or up to 4 kW for electric motors [Electric moped scooters (e-moped) are, according to the respective guidelines of the European Union, two-wheeled motor vehicles with design-related top speed of up to 45 km/h and displacement up to 50 cmor up to 4 kW for electric motors [ 9 ]. Since its inception in 2012, the e-moped sharing market has been growing year by year. The number of shared e-mopeds available worldwide increased by 164% in 2019 compared to 2018 [ 10 ] and by 58% in 2020 compared to 2019 [ 11 ]. In this study, e-moped sharing is defined as the shared use of electric moped scooters, where operators enable customers to rent scooters for short term directly through a smartphone application. Within these sharing services, providers ensure the e-mopeds are maintained and repaired as well as that their batteries are swapped and charged.
In a short time, new business models have developed on the basis of LEV, such as the e-moped, according to their mobility as a service approach. The dynamic development of the market led to questions about the environmental impact of e- moped sharing. In particular, the operational logistics of moped sharing services, such as the use of diesel vans to deliver batteries for swapping the e-moped batteries, remains highly questionable. In order to identify effective options against climate change, however, CO2 emissions from different modes of transport must be assessed in conjunction with usage scenarios considering the whole service and not only the vehicle itself. However, there is a lack of current and well-founded data on the CO2 emissions of e-mopeds that include reliable data on the product and operating concepts of shared mobility. In addition, the concepts of use may differ in the outcome.
Existing research focuses on life cycle assessments (LCA) of e-mopeds as a product by comparing it to alternative electric two wheelers [ 8 ] or to other transport modes [ 12 ]. Two existing LCA on e-mopeds focus on regional analysis on the effects of the private use of e-mopeds in Austria [ 13 ] and Switzerland [ 14 ].
Considering e-mobility in general, LCA studies on electric vehicles and their batteries on a product level are well established. Current studies provide a review of LCA studies of electric vehicles [ 15 ] as well as electric vehicles and their batteries [ 16 ] whereas other studies compare the impact of internal combustion engines and battery electric vehicles [ 17 ]. LCA studies on lithium-ion vehicle batteries are available [ 18 19 ] as well as LCA evaluating the effect of energy density on the impact of lithium-ion and lithium-sulfur batteries [ 20 ]. Life cycle assessments studying whole sharing services are more rare and focus on the energy use and greenhouse gas emissions of car sharing services in general [ 21 ] or on case studies of car sharing in specific cities [ 22 ]. Considering micromobility services, the LCA of shared stand-up scooters is well established. Existing studies evaluate the environmental impact of sharing services with stand-up scooters using case studies in the U.S. [ 23 ] and Berlin [ 24 ] or compare the use of shared stand-up scooters to private use and substituted transport modes [ 25 ]. Other LCA studies consider further techno-economical aspects [ 26 ] or the reliability of stand-up scooters [ 27 ] next to their environmental impact. A further study, of Gebhardt et al., analyzes the impact of shared stand-up scooters on the whole transport system [ 28 ] whereas the German Energy Agency evaluated scenarios regarding the future development of stand-up scooter sharing [ 29 ].
The environmental impact of e-mopeds in shared use has so far been considered only rudimentarily. Wortmann et al. analyzed e-moped sharing in Berlin based on a multi-agent transport simulation framework. However, indirect emissions of the sharing services, such as emissions caused by vehicles used for battery swapping were not considered [ 30 ]. De Bortoli recently conducted an LCA of shared micromobility and personal alternatives, including e-moped sharing. This study includes indirect emissions from the sharing service, but uses existing LCA models of e-mopeds from databases, so no distinct LCA of the e-moped product is implemented [ 31 ]. Overall, there is a gap in research regarding the LCA of e-moped sharing services based on a realistic use case of the production and use phase. Furthermore, there is a need to further research different usage and operation scenarios of e-moped sharing considering potential future developments.
In order to realistically model the impact of e-mopeds in sharing services, empirical data on the usage patterns are required. Current literature [ 32 ] suggests some usage patterns of micromobility modes like e-bike and stand-up scooter sharing are relatively well understood. There are studies analyzing usage patterns of free floating bike sharing in Singapore based on data generated from GPS sensors [ 33 34 ], in Shanghai, China based on data mining [ 35 ], or based on data scraping from public available API of sharing software in Nanchang, China [ 36 ]. Other studies focus on the usage patterns of shared stand-up scooters based on data sets of sharing providers, e.g., in Indianapolis, U.S. [ 37 ] as well as in Washington D.C. and in Austin, U.S. [ 38 ], whereas the analysis of stand-up scooter sharing in Taiwan, of Eccarius and Lu, is based on a survey [ 39 ]. Moreover, there are two studies comparing usage patterns of stand-up scooters and bike sharing in Washington D.C. based on data scraping [ 40 41 ]. However, there is a lack of empirical usage data for newer additions to the mobility sharing portfolio, such as e-moped sharing. Existing research concentrates mainly on the private use of mopeds, e.g., in the city of Munich, Germany [ 42 ] and in Australia [ 43 ]. Analyses of e-moped sharing are based on user surveys for data acquisition [ 44 ] or state market research as a data source [ 10 45 ].
2 savings in the transport sector. Germany, for example, has recently set a target to reduce CO2 emissions in the transport sector from 146 million tons of CO2 in 2020 to 85 million tons of CO2 in 2030 [Results of these analyses are needed, as most countries must achieve significant COsavings in the transport sector. Germany, for example, has recently set a target to reduce COemissions in the transport sector from 146 million tons of COin 2020 to 85 million tons of COin 2030 [ 46 ]. The addition of e-mopeds to micromobility services could contribute to those goals both cost effectively and rapidly.
This study examines whether e-moped sharing is eco-friendly compared to alternative transport modes and how the environmental impact can be further reduced. The paper aims to obtain the most accurate data possible that covers the entire usage concept, including product specifications. On the one hand, concrete data on the product is used based on the manufacturer’s bill of material and, on the other hand, data from a sharing provider on the use phase, offering a holistic view.
2 saving potentials of e-moped sharing systems and at the same time allow to decide on measures to better exploit the potentials. A schematic representation of the paper structure can be found inThis paper uses the LCA methodology to quantify the impact of e-scooter sharing on the impact category global warming potential (GWP 100), as well as additional impact categories that we present in the Appendix A . The paper not only conducts a product LCA but considers the usage patterns and operation logistics of sharing services. Therefore, we develop five different usage scenarios of e-moped sharing services for the use case of a German city based on the data set of a sharing provider as well as a literature review. The results help to implement a hot spot analysis, meaning the identification of the main triggers for negative environmental impacts, and to make recommendations for local authorities, manufacturers and sharing providers to reduce these impacts. Finally, we consider the results in the context of the entire transport system by comparing them with the environmental impact of alternative transport modes. The results offer governments a better assessment of the COsaving potentials of e-moped sharing systems and at the same time allow to decide on measures to better exploit the potentials. A schematic representation of the paper structure can be found in Figure 1