The long-run effects of urban air mobility: An urban spatial equilibrium assessment - PhDData

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The long-run effects of urban air mobility: An urban spatial equilibrium assessment

The thesis was published by Straubinger, Anna, in September 2022, VU University Amsterdam.


This dissertation makes use of urban spatial computable general equilibrium (USCGE) models tailored towards the application to urban transport in general and urban air transport specifically. The thesis assesses the effects of urban air mobility (UAM) introduction and hopes to contribute to the young field of research by providing evidence on possible long-run effects of transport drones. In recent years there has been a strong increase in research output in the field of UAM. The studies on the topic are wide-spread discipline-wise, reaching from vehicle design over UTM, regulation and certification to acceptance and adoption. Not only research, but also business activities are strongly increasing. Yet, UAM still faces technical, infrastructural and societal hurdles on the way to introduction. One of the main hurdles is the support of authorities, policy makers and the public. To enable a supporting environment early on, it is important to provide tools and methods that enable an assessment of the long-run effects of transport drones. Making use of USCGE models, this thesis broadens the discussion on UAM impacts to include also welfare effects, environmental aspects, and differentiate between the impacts on different parts of society. Applying a USCGE model to an existent transportation issue, namely parking, gives confidence in the chosen method. Tailoring the model to UAM and incorporating both high- and low-skilled households enables us to derive several interesting findings. Using agglomeration effects and amenities the model allows to differentiate between cities where high-skilled locate close to the city-centre and cities where high-skilled rather move to the suburbs. Differentiating between both initial spatial structures shows that the impact that the city structure has on the impact of UAM introduction, is minor. UAM system characteristics, like, land demand, prices, marginal cost or travel speed, in contrast significantly impact direction and magnitude of welfare effects. We find that the welfare effects for households with different income levels strongly differ and hence want to emphasise the relevance of understanding the differential impacts of UAM on user and non-users. Expanding the assessment to also include electric ground mobility and explicitly considering the environmental effects of UAM introduction shows that differences in taxation between gasoline and electricity lead to welfare losses when a forced transition from gasoline cars to electric cars takes place, while CO2 emissions go down. The higher tax on gasoline compared to electricity, as it is currently in place in Germany, results in a better internalization of otherwise untackled congestion externalities and hence explains this somewhat unexpected effect. The model also provides evidence, that introducing UAM as a substitute for gasoline cars has the potential to reduce CO2 emissions, whereas serving as an alternative to electric cars UAM usage increases CO2 emissions due to higher energy demand. Drones can also be used for cargo transport. In order to understand these effects as well the USCGE model is adapted to model different retail channels (local shops, online shopping and delivery via drone and online shopping with delivery via truck) and the logistic structures behind them. The assessment shows that additional retail channel choice options increase welfare and that the rise of e-commerce could significantly impact location choices in cities. This research shows that especially the long-run impact of passenger and cargo drones on users as well as non-users need consideration when assessing promising applications. From an environmental perspective, it is essential to identify applications that either allow to save energy due to shorter routes (e.g. due to geographical barriers), or justify the additional energy use due to the value added by the service (e.g. emergency applications or generating parity in living conditions).

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