Streamlining Energy Transition Scenarios to Key Policy Decisions

TL;DR

This paper introduces a machine learning approach using decision trees to simplify complex energy transition scenarios into key decision points, aiding policymakers in understanding and prioritizing critical factors for decarbonization.

Contribution

It presents a novel method to derive interpretable, qualitative storylines from quantitative energy models using decision trees, linking key decisions to transition outcomes.

Findings

High renewable deployment and sector coupling enhance scenario robustness.

Bioenergy, storage, and heat electrification are pivotal in Europe's fossil-free transition.

The approach effectively reduces complex model results into critical decision factors.

Abstract

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.