Streamlining Energy Transition Scenarios to Key Policy Decisions
This work addresses policymakers' difficulty in prioritizing key decisions from complex energy models, though it is incremental as it applies an existing ML method to a new domain.
The paper tackled the challenge of interpreting large sets of energy transition scenarios by using decision trees to derive interpretable storylines, showing that high renewables deployment and sector coupling make decarbonization robust against uncertainties.
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.