# Revealing design archetypes and flexibility in e-molecule import pathways using Modeling to Generate Alternatives and interpretable machine learning

arXiv: https://arxiv.org/abs/2512.23284 · HTML: https://mlsanity.com/paper/2512.23284/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 54 references that are in the map.
- First posted on arXiv 29 Dec 2025.

## Builds on (most cited first, 3 of 3)

- [The Near-Optimal Feasible Space of a Renewable Power System Model](https://mlsanity.com/paper/1910.01891/) (2019)
- [Streamlining Energy Transition Scenarios to Key Policy Decisions](https://mlsanity.com/paper/2311.06625/) (2023)
- [Modeling all alternative solutions for highly renewable energy systems](https://mlsanity.com/paper/2010.00836/) (2020)

## Abstract (arXiv)

Given the central role of green e-molecule imports in the European energy transition, many studies optimize import pathways and identify a single cost-optimal solution. However, cost optimality is fragile, as real-world implementation depends on regulatory, spatial, and stakeholder constraints that are difficult to represent in optimization models and can render cost-optimal designs infeasible. To address this limitation, we generate a diverse set of near-cost-optimal alternatives within an acceptable cost margin using Modeling to Generate Alternatives, accounting for unmodeled uncertainties. Interpretable machine learning is then applied to extract insights from the resulting solution space. The approach is applied to hydrogen import pathways considering hydrogen, ammonia, methane, and methanol as carriers. Results reveal a broad near-optimal space with great flexibility: solar, wind, and storage are not strictly required to remain within 10% of the cost optimum. Wind constraints favor solar-storage methanol pathways, while limited storage favors wind-based ammonia or methane pathways.