Energy system optimization finds the cheapest path to carbon neutrality. One answer. The answer depends on assumptions buried in the model — technology costs, demand projections, discount rates. Change an assumption, change the answer. This makes the optimal solution fragile: it's the best point in a landscape that we've measured poorly.
Kalweit, Fernandes, Alamia, and Victoria (arXiv:2603.23409) explore what's near the optimum instead of what's at it. They allow total system costs to increase by up to 5% and map the space of configurations that remain viable within this budget. The result is not one pathway but many — diverse combinations of carbon capture, conversion, storage, and direct removal that achieve European carbon neutrality at nearly the same cost.
The near-optimal space reveals which choices are robust and which are fragile. If a technology appears in every near-optimal configuration, it's necessary. If it appears in some and not others, it's substitutable — useful but not essential. The 5% cost margin separates structural requirements from contingent choices.
This inverts the usual relationship between optimization and decision-making. The optimal solution pretends to know more than it does — it presents one answer where many exist. The near-optimal space is honest about the uncertainty by showing which decisions survive it. A policymaker choosing among near-optimal configurations is making a political choice (which communities, which technologies, which timelines), not an engineering mistake.
The structural lesson applies beyond energy. Any high-dimensional optimization with uncertain parameters has a near-optimal space. The shape of that space — narrow or wide, connected or fragmented — is more informative than the optimal point itself.