Incorporating uncertainties towards a sustainable European energy system: A stochastic approach for decarbonization paths focusing on the transport sector

According to the Paris Agreement, the European energy system transition is essential to achieve GHG emission reduction targets set by the EU and limit the growth of global temperatures. Investigating the role of emerging mitigation technologies and their uncertainties together with the different GHG reduction targets is crucial to realize this transition. This study analyzes the uncertainties of electric vehicles' learning paths and biomass availability for biofuels, considering policy uncertainties. Since these technologies are considered possible alternatives to conventional fuels to reduce CO2 emissions in transport, it is critical to understand their future role and the possible impact of their uncertainties during the European energy system design in case of different climate ambitions. Stochastic modeling is applied to analyze associated uncertainties as an additional approach to a traditional sensitivity analysis. Our results show that decarbonization of car transport is prioritized, and electric cars appear as no-regret options in the sector's design during the energy transition. Therefore, early deployments of EVs are essential to hedge the given uncertainties independent of the hedging period's length. Longer resolution time reduces the deployment of electric vehicles in the recourse strategies compared to having a shorter one due to a delay in the cost reductions. This decline becomes more evident with the stochastic analysis. The policy uncertainty of decarbonization targets has the highest impact on the studied uncertainties on the development of the transport sector. The transport sector can show faster adjustments considering the technology portfolio's shorter lifetime. Thanks to this adjustment, the sector depicts higher decarbonization in the hedging as well as in the recourse strategies.