Streamlining Energy Transition Scenarios to Key Policy Decisions
Florian Joseph Baader Stefano Moret Wolfram Wiesemann Iain Staffell André Bardow
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.