# Streamlining Energy Transition Scenarios to Key Policy Decisions

arXiv: https://arxiv.org/abs/2311.06625 · HTML: https://mlsanity.com/paper/2311.06625/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 51 references that are in the map.
- First posted on arXiv 11 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [The Near-Optimal Feasible Space of a Renewable Power System Model](https://mlsanity.com/paper/1910.01891/) (2019)

## Built on by (most cited first, 2 of 2)

- [Identifying Dealbreakers and Robust Policies for the Energy Transition Amid Unexpected Events](https://mlsanity.com/paper/2503.05735/) (2025)
- [Revealing design archetypes and flexibility in e-molecule import pathways using Modeling to Generate Alternatives and interpretable machine learning](https://mlsanity.com/paper/2512.23284/) (2025)

## Abstract (arXiv)

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.