A detailed assessment of a low energy demand, 1.5 ^∘C compatible pathway is provided for Europe from a bottom-up, country scale modelling perspective. The level of detail enables a clear representation of the potential of sufficiency measures. Results show that by 2050, 50% final energy demand reduction compared to 2019 is possible in Europe, with at least 40% of it attributable to various sufficiency measures across all sectors. This reduction enables a 77% renewable energy share in 2040 and 100% in 2050, with very limited need for imports from outside of Europe and no carbon sequestration technologies. Sufficiency enables increased fairness between countries through the convergence towards a more equitable share of energy service levels. Here we show, that without sufficiency measures, Europe misses the opportunity to transform energy demand leaving considerable pressure on supply side changes combined with unproven carbon removal technologies.

Studying a large number of scenarios is necessary to consider the uncertainty inherent to the energy transition. In addition, the integration of intermittent renewable energy sources requires complex energy system models. Typical days clustering is a commonly used technique to ensure the computational tractability of energy system optimisation models, while keeping an hourly time step. Its capability to accurately approximate the full-year time series with a reduced number of days has been demonstrated (i.e., a priori evaluation). However, its impact on the results of the energy system model (i.e., a posteriori evaluation) is rarely studied and was never studied on a multi-regional whole-energy system. To address this issue, the multi-regional whole-energy system optimisation model, EnergyScope Multi-Cells, is used to optimise the design and operation of multiple interconnected regions. It is applied to nine diverse cases with different numbers of typical days. A bottom-up a posteriori metric, the design error, is developed and analysed in these cases to find trade-offs between the accuracy and the computational cost of the model. Using 10 typical days divides the computational time by 8.6 to 23.8, according to the case, and ensures a design error below 17%. In all cases studied, the time series error is a good prediction of the design error. Hence, this a priori metric can be used to select the number of typical days for a new case study without running the energy system optimisation model.