The influence of E-scooters on urban mobility and transport related emissions.

Abstract

In the efforts made to reach a sustainability transportation system in large urban cities, shared micro mobility system (𝑆𝑀𝑀𝑆) appears in many countries including Sweden to solve the social, economic, and environmental challenges, and to promote this kind of transport instead of other transport modes. Nowadays, the increasing demands coming from both population growth and urbanization lead to consider all available alternatives inside the transportation sectors. The invention of E-scooters in the last few years was a beginning of a new advanced shared mobility mode which takes place in the urban transport system in most large cities. The new innovative shared e-scooters are paying more attention to their environmental impacts and urban infrastructure by looking to their influences on other transport mobility modes. The main purpose of this paper is to calculate the life cycle analysis (LCA) of climate change Greenhouse Gas (GHG) emissions which represents the biggest environmental impacts. Other challenges are depending on the social behaviour studies and survey analysis of mode substitution patterns to use E-scooters as a preferred option based on the mode choice models. To reach this goal, Life Cycle Analysis (LCA) of the Voiager model (𝑉5) from VOI company has been made, and then the transaction data from both companies (Voi and Tier) have been handled to understand how to reduce the emissions in the urban transportation system and evaluate LCA results which contain more than 700 000 trips of shared e-scooters in Gothenburg city of Sweden. The emission results from LCA analysis are calculated to be more than the average estimated emissions from previous literature which is equal to 151 (𝑔. 𝐶𝑂2 𝑒𝑞. ) compared to 70 (𝑔. 𝐶𝑂2 𝑒𝑞. ) per one e-scooter. These results are also compatible with the previous literature when the manufacturing stage of LCA contributes to more than fifty percent of the total LCA emissions. Other results from statistical analysis of data collected in Gothenburg city by using (Jupyter notbook, Python 3), find the highest probability of substituted transport mode by shared e-scooter is “Walk” while the lowest probability is “Car” in the inner city of Gothenburg which explain the negative environmental impacts of shared e-scooter occurred when e-scooter is replaced by the short trip distance of “Walk”. In addition to calculate the aggregated 𝐺𝐻𝐺 reduction per unit area for each zone of Gothenburg and classify these zones according to their environmental impacts.