Joris Simaitis

Anticipating the Environmental Impacts of Automotive: Integrating Future Energy Systems into Prospective Life Cycle Assessment

In response to the climate emergency, transport is transitioning from Internal Combustion Engine Vehicles (ICEVs) to Electric Vehicles (EVs). Despite concerns about toxicity and resource depletion, Life Cycle Assessment (LCA) literature suggests that on average, EVs reduce whole Greenhouse Gas (GHG) emissions by up to 45%. However, EVs have a complex life cycle. The supply chain relies on a diverse range of raw materials produced upstream before manufacture, while post-manufacture, the use stage extends potentially up to 20 years before reaching its end-of-life for recycling. Hence, the environmental impacts are temporally distributed across the life cycle stages. To date, automotive LCA relies on historic data and arbitrary methods that are limited in their ability to capture how future impacts will evolve. Therefore, there is considerable uncertainty about the future consequences of the uptake of new technology such as EVs, adding risk and ambiguity to whether the automotive landscape is moving towards the sustainability agenda.

This PhD project develops prospective methodology that combines the LCA framework with Integrated Assessment Models (IAMs) to anticipate how future environmental impacts of automotive technology will evolve. IAMs consider potential socioeconomic development pathways to optimize outputs such as what future energy mix scenarios may look like. Advanced LCA techniques in Python are developed to incorporate IAMs into LCA inventories, providing the ability to explore future impacts of markets, production processes, and technology. These processes are then used to explore the long-term environmental impacts of automotive that account for changes in upstream production, temporal distribution across life-cycle stages, and potential downstream pathways for end-of-life recycling.