Shared, autonomous electric vehicles (SAEVs) are expected to enter the market in the coming decades. Using MATSim, we simulate a use case where SAEVs are introduced in multiple suburban zones at the outskirts of Vienna (Austria), which are characterized by relatively low population density, but have access to at least one rail-based public transport stop. For all combinations of different fleet sizes and fare levels, we find that a relatively small share of car trips by residents of these zones (7 to 14 percent) are replaced by SAEVs, generating CO2 emissions reductions of 5 to 11 percent. Moreover, 23 to 35 percent of trips previously undertaken by foot or bicycle are replaced by SAEVs, as well as 10 to 20 percent of public transport trips. The potential of SAEVs to reduce the use and ownership of private vehicles in suburban areas therefore seems to be rather limited, which is also reflected in our finding that one SAEV usually replaces only 2 to 4 private vehicles. The potential becomes somewhat larger when the usage and ownership of private cars is assumed to become more expensive, leading to 17 to 20 percent of car trips being replaced by SAEVs and generating CO2 emissions reductions of up to 32 percent.

Climate change disproportionately impacts capital and output in low- and middle-income countries (LMICs). Limited fiscal space and high dependence on capital good imports further curtail their ability to make timely climate-resilient investments. In this paper we present a demand-driven model that is supply-side constrained due to insufficient build up of production capacity. Calibrating the model to Fiji, we evaluate growth pathways for three climate futures – 2C, 3C, and 4C global warming by the end of the century. We evaluate the role of a public climate fund to enable partial recovery that is financed through four different schemes – debt-led recovery, higher tax on households, higher taxes on capitalists, and unconditional grants from the rest of the world. Recovery is possible in the 2C scenario, but the 3C and 4C scenarios increasingly face higher investment costs in the face of lower growth and saving rates. In the 4C scenario, even the most generous unconditional grants scheme fails to prevent the downward spiral of hitting capacity constraints despite an initial boost to output. These insights underscore the need for effective and equitable domestic climate policies and affordable finance and compensation to support sustainable development in vulnerable countries.

The paper simulates hourly variations in the sources of, and exposure to, traffic-related PM10 emissions for the city of Vienna, Austria. Using an extended and calibrated MATSim micro-simulation model, we reproduce agent-level mobility patterns for a representative day and track the use of different travel modes and time spent at different location types. Hourly street-level PM10 emissions, mostly caused by cars, are extrapolated for the entire city. Simulations show high exposures during morning and evening travel peaks, especially at work, education, and home locations that also exceed the recommended 50 μg/m³ threshold. Among various socioeconomic status (SES) groups, urban, single, and those living near the city center face above-average exposures, while car users, which cause majority of the emissions, are relatively less exposed. Finally, we show that Shared Autonomous Electric Vehicles (SAEVs) reduce PM10 emissions, but the benefits are not homogeneously distributed across different SES groups.