  Uncertainties surrounding the energy transition often lead modelers to
present large sets of scenarios that are challenging for policymakers to
interpret and act upon. An alternative approach is to define a few qualitative
storylines from stakeholder discussions, which can be affected by biases and
infeasibilities. Leveraging decision trees, a popular machine-learning
technique, we derive interpretable storylines from many quantitative scenarios
and show how the key decisions in the energy transition are interlinked.
Specifically, our results demonstrate that choosing a high deployment of
renewables and sector coupling makes global decarbonization scenarios robust
against uncertainties in climate sensitivity and demand. Also, the energy
transition to a fossil-free Europe is primarily determined by choices on the
roles of bioenergy, storage, and heat electrification. Our transferrable
approach translates vast energy model results into a small set of critical
decisions, guiding decision-makers in prioritizing the key factors that will
shape the energy transition.
